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okf-mcp-server

by lorsabyan

Read one OKF concept

okf_get_concept
Read-onlyIdempotent

Read a full OKF concept by ID: get frontmatter, trust and lifecycle signals, provenance, links, and body in one call, so you can verify definitions and cite sources.

Instructions

Read a single concept in full: its frontmatter, trust and lifecycle signals, provenance, body, and links.

Args:

  • bundle_path (string): directory containing the bundle

  • id (string): concept id — the file path without .md, e.g. "metrics/revenue"

  • include_body (boolean): default true; set false for metadata only

  • response_format ('markdown' | 'json'): default 'markdown'

Returns: { "id": string, "title": string, "type": string, "description": string, "tags": string[], "resource": string, "status": ..., "trust": ..., "verified_by": string[], "verified_at": string, "updated_at": string, "stale_since": string, "generated": { "by": string, "at": string }, "sources": [{ "id": string, "resource": string, "title": string, "author": string, "lastModified": string, "usageCount": number }], "computation": { // present only for type "Attested Computation" "runtime": string, "parameters": [{ "name": string, "type": string, "required": boolean }], "path": string, "executor": { "resource": string, "receipt": string[] }, "attester": { "resource": string } }, "links_to": string[], "cited_by": string[], "body": string // omitted when include_body is false }

Trust and freshness are worth checking before relying on a definition: "trust" is derived from verified (§5.3) and "stale_since" from stale_after (§5.5). They are advisory signals, not access control.

For an Attested Computation, the caller may supply values for the declared parameters ONLY, and must never author or rewrite the computation itself — that boundary is the point of the type (§10).

Examples:

  • Use when: "How is revenue defined?" -> id="metrics/revenue"

  • Use when: you need a concept's sources to cite it

Error Handling:

  • An unknown id returns near-miss suggestions from the bundle

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesConcept id: path without .md, e.g. "metrics/revenue"
bundle_pathYesPath to the OKF bundle directory (a folder of .md files). Absolute paths are safest.
include_bodyNoInclude the markdown body
response_formatNo'markdown' for reading, 'json' for machine processingmarkdown
Behavior5/5

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

Beyond the readOnlyHint and idempotentHint annotations, the description adds meaningful behavioral context: trust and stale_since are advisory signals, not access control; for Attested Computation the caller must not author or rewrite the computation, only supply parameter values; unknown IDs return near-miss suggestions. These details disclose important behavioral nuances.

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

Conciseness4/5

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

The description is organized into clear sections (Args, Returns, Trust note, Computation note, Examples, Error Handling) and front-loaded with the core purpose. It is lengthy, but the detailed return schema and caveats are necessary given the absence of an output schema. No redundant fluff.

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?

Given the tool's complexity (trust signals, computation type, various fields) and no output schema, the description is exceptionally complete. It documents the full return structure, error behavior, usage examples, and critical trust/computation caveats. Everything an agent needs to invoke and interpret the tool is present.

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

Parameters3/5

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

All four tool parameters are already well-documented in the schema (100% coverage), so the description's parameter list adds little new semantic value. The note about 'declared parameters ONLY' refers to the computation's parameters, not the tool's own parameters, so it doesn't elevate the score.

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 starts with 'Read a single concept in full' and enumerates the content (frontmatter, trust and lifecycle signals, provenance, body, links). This specific verb+resource clearly distinguishes it from siblings like okf_list_concepts (listing) and okf_search (searching).

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

Usage Guidelines4/5

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

The description provides concrete use cases ('How is revenue defined?' -> id='metrics/revenue' and 'need a concept's sources to cite it'), which clearly indicate when to use the tool. It does not explicitly name sibling tools or state when not to use it, but the examples and 'single concept' framing imply appropriate usage.

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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