Skip to main content
Glama
angrysky56

Cognitive Diagram Navigation MCP Server

by angrysky56

export_proof

Export a diagram's transformation history as a structured proof in text or JSON. Use it to verify how a diagram reached its current state.

Instructions

Export a diagram's transformation history as a structured proof.

Args: diagram_id: ID of diagram to export proof for output_format: 'text' (natural language) or 'json' (structured)

Returns: dict with proof steps and metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
diagram_idYes
output_formatNotext

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. 'Export' implies a read-only operation and the Returns line indicates proof steps plus metadata, which is useful, but there is no disclosure of permissions, whether the proof generation is expensive, or failure modes.

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?

Front-loaded purpose sentence followed by compact Args/Returns blocks. Every line earns its place, though the Args/Returns formatting is slightly formulaic given an output schema already exists.

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

Completeness4/5

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

An output schema exists, so return-value explanation is largely redundant but harmless. The description covers both parameters and the operation's scope; only the missing usage/routing guidance keeps it from being fully complete.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate, and it does: it explains diagram_id and, more importantly, enumerates the output_format values ('text' natural language vs 'json' structured) that the schema leaves as a bare string with only a default.

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

Purpose4/5

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

States a specific verb (export) and resource (a diagram's transformation history) plus the output form (structured proof). It clearly distinguishes itself from siblings like diagram_extract or compute_metrics, though it never explicitly names an alternative to route against.

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

Usage Guidelines2/5

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

No guidance on when to use this versus diagram_extract, explore_reasoning_space, or pattern_match. No prerequisites or exclusions are stated; an agent must infer the context from the verb alone.

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