# MCP tools reference > Every customer-facing tool in the KnitKnot MCP server: what each does, its parameters, and what it returns, so you or your AI assistant know exactly which tool to call. - Section: Reference - Updated: 2026-08-24 - Canonical: https://knitknot.ai/docs/mcp-tools/ - Publisher: KnitKnot, Get Agent Ready (https://knitknot.ai) --- The authenticated KnitKnot MCP server exposes your authorized workspace to AI assistants via the [Model Context Protocol](https://modelcontextprotocol.io/). Connecting is covered in [Connect to your AI tools](/docs/connect-to-ai-tools/). This server is separate from the read-only `/mcp` interface that is rolling out on managed customer domains. The surface is deliberately lean and REST-style: `list_*` returns a paginated index (scalars only), a matching `get_*` drills into one entity, and there is one write tool to act. Coverage matrices, the topic ontology, and the question library are console concerns; their conclusions are distilled into ranked `list_issues` (diagnose) and `list_playbooks` (fix) responses. Paginated responses carry `pagination: {has_more, offset}`; page by passing `offset`. Every call returns JSON scoped to **your** workspace, and a status change made via an assistant appears in the console. ## Orient ### `get_workspace_context` Company, subjects (brands + products), competitors, capabilities, buyer roles, and topics. Call first to orient the assistant before a task. No parameters. ## How am I doing, and who's the threat? ### `competitive_overview` Your AI Presence Score, W/L per competitor (sorted by threat), per-engine performance, top capability gaps, headline metrics (visibility, share of voice, average rank, sentiment), and brand coherence. A current snapshot that enumerates competitors, so it subsumes a separate competitor list. **Params:** `subject_name` *(opt)* : product/brand name to scope to; omit for workspace-wide. ### `get_competitor` One competitor deep dive: research profile (capabilities, positioning, differentiators), head-to-head W/L, per-engine W/L/T, per-capability W/L, top cited sources. **Params:** `competitor_name` *(req)* : name from `competitive_overview`. ## Diagnose → prove → fix → act ### `list_issues` The workspace's Issues : the ranked diagnostic catalog of what AI gets wrong about you or where you're losing, most-important-first. Each carries an `importance_score` (0-100). Light index; call `get_issue` for the proof, action, and linked playbooks. **Params:** `status` *(opt)* `candidate`|`open`; `issue_type` *(opt)* `misrepresentation`|`feature_gap`|`visibility_gap`|`source_gap`; `subject_name` *(opt)*; `limit` *(opt, default 20, max 50)*; `offset` *(opt)*. ### `get_issue` One issue's verbatim proof + what to do. - **Answer-grain** (visibility/competitor gaps): the backing answers : each row's `evaluation_id`, `prompt_text` (the buyer question), `engine`, `outcome`, `coverage`. Pass an `evaluation_id` to `read_evaluation` for the full AI answer. - **Claim-grain** (misrepresentation, source leak, negative sentiment, capability loss): the exact claims AI made : `verbatim_quote`, verdict, severity, engine, source URL. The API retains its established feature-oriented enum values. - Plus `recommended_action`, `fixability` (grounded = editable page vs parametric = baked into the model), and `linked_playbooks`. Want the questions that **track** a fix? Each `linked_playbook` has an `id` : pass it to `get_playbook` with `include=["questions"]` for its `tracked_prompts` (worst first). **Params:** `issue_id` *(req)* : from `list_issues`. ### `read_evaluation` (read an Answer) The full AI answer behind one scored Answer: the raw `response_text` plus the judge's extraction : `score`, `competitive_outcome`, `positioning_accuracy`, `sentiment`, capabilities mentioned/missing, `recommendation`. The API keeps its established `read_evaluation`, `evaluation_id`, and `tracked_prompts` field names even though the console labels these objects Answers and Questions. Id-anchored only (no free-form workspace search) so the agent stays on the diagnose → prove → fix path. Responses >6,000 chars are truncated (`truncated: true`, `response_length` total) : hand the id to your client's fetch tool for the unabridged text. **Params:** `evaluation_id` *(req)* : from `get_issue`'s `example_evaluations` or `get_playbook`'s `tracked_prompts`. ### `list_playbooks` The Playbooks : the fixes for your issues : ranked highest-ROI-first. Defaults to active work (`proposed`, `in_progress`). Each carries its ramp `phase` (`fix-now`, `create-for-coverage`, `deepen`, `earn`) and `phase_label`. The response top level carries the workspace's ramp `stage` (`estimated: true` when computed on partial data : treat the roadmap as provisional) and a `generating_count` of playbooks still being drafted. **Params:** `statuses` *(opt, default `["proposed","in_progress"]`)* : pass `["shipped"]` to see shipped (`dismissed` is internal-only); `playbook_types` *(opt)* `comparison_page`|`feature_page`|`correction`|`third_party`|`refresh`|`icp_content`; `limit` *(opt, default 20, max 50)*; `offset` *(opt)*. ### `get_playbook` Everything needed to **draft** the content for one playbook : most importantly the exact buyer keywords and terms to write with. Sections selected by `include`; omit it for the full drafting payload (keywords, brief, page-to-beat, critique, receipts). The `questions` section is the questions the play is measured against (`tracked_prompts`, worst first) : opt in with `include=["questions"]`. This is how an agent finds the questions linked to an issue once it lands on the play that addresses it, and where the `evaluation_id`s for `read_evaluation` also live. **Params:** `playbook_id` *(req)* : from `list_playbooks` or a `linked_playbook.id`; `include` *(opt)* : subset of `keywords`, `brief`, `page_to_beat`, `critique`, `receipts`, `questions`. Omit for the full set excluding `questions`; pass a narrow subset to fetch one slice fast. ### `update_playbook_status` Update a playbook's status : the one write tool. Changes appear in the console. **Params:** `playbook_id` *(req)*; `new_status` *(req)* `in_progress`|`shipped`|`dismissed`; `shipped_url` *(req when shipping content plays)* : URL where content was published (earn-verb plays ship as "outreach sent" with no URL; an optional evidence link is stored but never treated as an owned page); `dismissed_reason` *(req when dismissing)* : `ignored`, or a granular `irrelevant`|`wrong_evidence`|`already_done`|`bad_target`|`too_much_effort`|`other`. --- That's the full set. Internal (superadmin-only) tools : coverage matrices, the topic ontology, the question library listing, score-trend history, and more : are hidden from customer callers by the curation allowlist and not listed here. One can be promoted to the customer surface deliberately when a real agent workflow emerges for it. Next: how gaps become [issues and playbooks](/docs/issues-and-playbooks/), or back to [connecting your AI tools](/docs/connect-to-ai-tools/).