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Clueso

by io.clueso
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Get clueprint

get_clueprint
Read-only

Fetch clueprint data. Use include to control how much you pull back:

  • include="metadata": just the clueprint's name/description/tags/visibility/thumbnail.

  • include="tree": metadata + the full file tree (path, mime_type, and size per entry). Call this first when exploring an unfamiliar clueprint.

  • include="files": the contents of the files listed in file_paths (no metadata — call include='tree' first if you also need metadata). Text inline, binary as presigned GET URLs.

  • include="all": metadata + the file tree + the contents of every file in the clueprint (use sparingly on large clueprints).

Calling this also logs the clueprint as "used" (fire-and-forget) so the workspace's recents list stays accurate — no follow-up use_clueprint call needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
includeNoWhat to return: 'metadata' | 'tree' | 'files' | 'all'. Defaults to 'tree'.tree
file_pathsNoinclude='files' only: relative paths to read (e.g. ['design/colors.md', 'rules/voice.md']). Ignored for other modes.
clueprint_idYesID of the clueprint.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • removedInput schema / properties / context
      Removed value: -{
-  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
-  "type": "string"
-}
    • removedInput schema / properties / conversation_id
      Removed value: -{
-  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
-  "type": "string"
-}
    • removedInput schema / properties / llm_model
      Removed value: -{
-  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
-  "type": "string"
-}
    • changedInput schema / required
      Previous value: -[
-  "clueprint_id",
-  "context",
-  "llm_model"
-]New value: +[
+  "clueprint_id"
+]
  2. Changed4 schema fields changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}
    • addedInput schema / properties / conversation_id
      Added value: +{
+  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
+  "type": "string"
+}
    • addedInput schema / properties / llm_model
      Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "clueprint_id"
-]New value: +[
+  "clueprint_id",
+  "context",
+  "llm_model"
+]
  3. Changed2 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
  4. First observed

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint annotation, the description discloses a real behavioral side effect: calling this tool logs the clueprint as 'used' in a fire-and-forget manner. It also explains return behavior clearly — text inline, binary as presigned GET URLs — which is essential since there is no output schema to cover this.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is organized as a scannable bulleted list with the opening summary first. Each bullet adds distinct information — mode semantics, ordering advice, return-format details, and side effects — with no redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and an enum whose values drastically change return shape, the description carries the full burden and succeeds. It specifies what comes back in every mode, how file contents are represented, when file_paths is used, and what side effect occurs, so an agent has everything needed to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents all three parameters, but the description adds substantial meaning: what each include value actually returns, that file_paths is only honored in 'files' mode, the relative-path example, and the default value of 'tree'. This goes well beyond the baseline schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource — 'Fetch clueprint data' — and then expands into four distinct include modes that make the tool's exact behavior clear. It is plainly distinguishable from create_clueprint and update_clueprint siblings, which are mutations rather than fetches.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit guidance for each include mode: use 'tree' first when exploring, call 'tree' before 'files' if metadata is needed, and use 'all' sparingly on large clueprints. It even tells the agent that no separate use_clueprint follow-up is required, resolving a potential ambiguity about how usage is recorded.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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