thought-graph-mcp

by jeff-ong

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clear, distinct purpose: starting a session, adding steps, revising, finalizing, and retrieving sessions. No overlap or ambiguity.
Naming Consistency5/5
All tools use underscore_case with a verb_noun or verb_gerund pattern (e.g., add_thought, begin_thinking, list_sessions), maintaining consistency throughout.
Tool Count5/5
6 tools is well-scoped for a reasoning graph system, covering core operations without unnecessary bloat.
Completeness4/5
Covers the essential lifecycle: start, add, revise, finalize, and retrieve. Missing explicit deletion of sessions or thoughts, but these are minor gaps.
Average 3.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description only states it lists sessions from disk. It does not clarify that this is a read-only operation, nor does it mention any potential side effects, performance implications, or handling of empty results.
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?
A single, well-structured sentence that immediately conveys the tool's purpose. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no parameters and no output schema, the description is minimal but adequate. It could be improved by clarifying behavior when no sessions exist or if results are ordered.
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?
No parameters exist, so baseline is 4. The description adds context about what the tool lists (sessions with id, title, step count) beyond the empty schema.
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 clearly states the tool lists existing Thought Graph sessions with specific fields (id, title, step count) from disk. It distinguishes from siblings like get_session (single session) and add_thought (add).
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 tool versus alternatives. For example, it does not mention that get_session is for retrieving a single session or that sibling tools are for other operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It mentions recording an answer and refreshing artifacts, but does not explain side effects, overwriting behavior, or what 'refresh the artifacts' entails. This leaves some gaps for an agent reasoning about state changes.
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 extremely concise with just two sentences. Every phrase adds value: the action, the scope (session), the secondary effect (refresh artifacts), and the usage context. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the primary action and usage context, but lacks details on return values, confirmation, or error conditions. Given no output schema and no annotations, the agent may need more information to handle edge cases or verify success.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no semantic detail for 'sessionId' or 'answer'. While the parameter names are somewhat self-explanatory, the description should clarify the expected format or constraints for 'answer', especially given no enums or additional schema guidance.
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 clearly states the tool records the final synthesized answer and refreshes artifacts, using specific verbs and resources. It distinguishes 'finalize' from sibling tools like 'add_thought' and 'revise_step' by framing it as the final action after a conclusion is reached.
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 gives specific timing guidance: 'Call after the graph supports a conclusion.' While it doesn't explicitly list when not to use it, this instruction clearly differentiates it from more intermediate siblings like 'add_thought' or 'begin_thinking'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavior. It states the tool returns JSON and artifact paths, implying a read-only operation. However, it does not mention permissions, error handling, or side effects, which would be beneficial.
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 two sentences: first defines the tool's output, second gives usage guidance. Every word adds value, with no repetition or fluff.
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?
For a simple retrieval tool with one required parameter and no output schema, the description covers the key outputs (graph JSON with nodes/edges/statuses, artifact paths) and suggests use cases. Could be slightly improved by noting what happens if sessionId is invalid.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'sessionId' is a string with no description in the schema (0% coverage). The tool description adds no explanation about what sessionId represents (e.g., 'the ID of the session to retrieve'), leaving the agent to infer from context.
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 clearly states the verb 'Return' and the resource 'current reasoning graph as JSON' along with on-disk artifact paths. It distinguishes from sibling tools like list_sessions (which lists sessions) and add_thought (which modifies graph).
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 explicitly advises using this tool 'to review where things stand or recover the exact graph file path,' providing clear context. It does not explicitly state when not to use it or mention alternatives, but the siblings list helps differentiate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It states the tool supersedes and replaces a node and returns downstream nodes, but omits prerequisites, error conditions, or authorization needs. Some behavioral info present but incomplete.
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?
Two sentences, no filler. Action and output are front-loaded. Every word is meaningful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description covers main action and output but lacks detail on error scenarios, prerequisites, or the exact nature of 'downstream nodes'. Adequate but with notable gaps.
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?
Schema description coverage is 75% (3 of 4 parameters have descriptions). The description adds marginal value by restating nodeId purpose and relating newContent to replacement, but does not enhance parameter understanding beyond the schema. Baseline 3 applies.
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?
Description clearly states the tool pinpoints a step by node ID, marks it superseded, and replaces it with a fresh revision node. It also mentions returning downstream nodes for re-examination. This distinguishes it from sibling tools like add_thought.
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?
Explicit guidance: 'Use when the user points at a step or you find a flaw.' Does not mention when not to use or alternatives, but context with siblings is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that the tool returns a sessionId and protocol instructions and initiates a new session. However, it does not mention potential side effects, idempotency, or any constraints (e.g., session limits). The disclosure is adequate but not exhaustive for a creation tool.
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 extremely concise, consisting of only two sentences. The first sentence states the purpose, and the second provides crucial sequential guidance. No redundant or extraneous information is present, making it highly efficient.
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?
For a tool with only two parameters and no output schema, the description is sufficiently complete. It explains the tool's role as the entry point and mentions return values. While it doesn't elaborate on the protocol instructions, this is acceptable given the likely complexity of a reasoning session starter. The description covers the essential information.
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?
The input schema covers both parameters with descriptions, achieving 100% coverage. The description does not add information beyond what the schema provides (the default behavior for title is already in the schema). Therefore, the description adds no extra value to parameter semantics, resulting in a baseline score of 3.
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 clearly identifies the tool's action: starting a new Thought Graph reasoning session. It specifies the verb 'Start' and the resource 'Thought Graph reasoning session', and distinguishes itself from siblings by stating 'Call this FIRST, then build the reasoning with add_thought.' This provides a specific, unambiguous purpose.
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 gives explicit sequential guidance: 'Call this FIRST, then build the reasoning with add_thought.' This indicates when to use the tool relative to its siblings. However, it does not explicitly state when not to use it or mention alternatives beyond the sequential hint, which is a minor gap for a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that adding a thought creates a graph node and links with parents, but does not mention side effects, persistence, permissions, or limits. The description is adequate but not deeply transparent.
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 extremely concise with three sentences. It front-loads the main action and immediately provides key guidance. Every sentence adds value without redundancy.
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?
Given the tool complexity (7 params, 4 required, enum, array) and lack of output schema, the description covers the core functionality well. It misses some behavioral details (e.g., return value), but overall is fairly complete for agent usage.
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 high (86%), so baseline is 3. The description adds value by explaining how 'parents' are used for linking and merging, and reinforces the 'type' constraint. It does not duplicate schema information unnecessarily.
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 clearly states the tool's action ('Record') and resource ('one reasoning step (a graph node)'). It differentiates from sibling tools by focusing on adding nodes vs. revising or finalizing. It also specifies constraints like 'keep it to a single idea'.
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 explicit guidance on usage, such as using 'parents' to link nodes, merging branches, and creating sibling nodes. It implies when to use (to add a reasoning step) but lacks explicit exclusions or direct comparisons to siblings like 'revise_step' (though noted in schema).
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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