MeshMind

Server Quality Checklist

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Latest release: v1.1.0
Disambiguation4/5
Tools have largely distinct purposes, with some overlap between crush_file and get_optimized_context as both compress, but they target different use cases (file shortcut vs. general pipeline). export_codebase_graph and scan_local_codebase are related but differ in output format and depth. Overall, agents can distinguish most tools.
Naming Consistency5/5
All tool names use snake_case with a consistent verb_noun or descriptive pattern (e.g., crush_file, get_optimized_context, research_last_30_days). No mixing of conventions, making naming predictable and clear.
Tool Count5/5
Seven tools cover the advertised functionality—context compression, codebase analysis, and research—without being excessive. Each tool has a defined role, and the count is appropriate for the server's purpose.
Completeness4/5
The tool set covers key operations: compression, decompression, stats, codebase scanning, graph export, and research. Minor gaps exist, such as no tool to list or manage stored refs, and no direct configuration update, but core workflows are well-supported.
Average 3.9/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 10 commits in the last 12 weeks
- Last stable release on June 16, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the scanning and export behavior, and specifies output formats, but does not mention potential side effects, performance impact, or limitations like directory depth or file exclusions.
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, clear sentence that front-loads the core purpose and includes critical details (format options). No wasted words.
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 tool with two parameters and no output schema, the description adequately covers purpose and output options. However, it lacks details on the shape of the JSON output or any return value structure, which could be helpful for agents.
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 coverage is 100%, so both parameters are already documented. The description adds context about output format specifics (Mermaid vs JSON) but does not significantly exceed schema detail.
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 states a specific verb ('scan and export') and resource ('codebase graph'), clearly distinguishing it from sibling tools, which focus on context retrieval and file operations.
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 explicit guidance on when to use this tool versus alternatives like 'scan_local_codebase' or 'retrieve_context'. The usage is implied through the specific output formats, but lacks when-not-to-use or prerequisite information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses multi-source fetching, trailing window, relevance-reranking, deduplication, and a compress option. It does not cover rate limits, authentication, or output format, but the key behaviors are well-described.
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, front-loaded with the main purpose and key features. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite tool complexity (multiple sources, parameters, no output schema), the description lacks details on output format, return values, or limits. It mentions reranking and dedup but does not explain what the results look like, making it incomplete for an agent to fully understand the tool's behavior.
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 100%, so each parameter has a schema description. The tool description adds general behavioral context (e.g., reranking, dedup) but does not significantly enhance parameter understanding beyond what the schema already provides.
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 specifies the verb 'Fetch' and the resource 'recent community/social signal on a topic' from a defined set of sources. It distinguishes from siblings by listing specific sources and mentioning a trailing window filter, which is unique among the sibling tool names.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for fetching recent social signals on a topic but does not explicitly state when to use this tool versus siblings like 'retrieve_context' or 'scan_local_codebase'. No exclusions or alternatives are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, description fully explains behavior: returns two categories of stats (session and lifetime), lists fields, and notes persistence under MESHMIND_HOME. No side effects or permissions needed are implied, which is sufficient for a read-only stats tool.
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?
Single sentence is efficient but slightly dense. Front-loaded with the main action, but could be broken into two sentences for clarity.
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 zero-parameter, no-output-schema tool, description adequately explains return values (session vs. lifetime fields). Covers all listed fields, making it usable for an agent to interpret results.
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; description does not need to add param info. Baseline score of 4 is appropriate as schema coverage is 100% and description adds value by explaining return structure.
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 it returns token-savings stats with specific fields (session and lifetime scopes). It distinguishes from sibling tools like crush_file and get_optimized_context by focusing on statistics rather than compression or retrieval.
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 over alternatives. It does not mention prerequisites, complementary tools, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the output types (compact summary vs. full JSON map) and lists the analysis components. It does not cover performance or permissions, but for a read-only scan tool, the transparency is strong.
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 two sentences: the first lists the analysis features, the second explains output options. It is concise and front-loaded with the main action, though the dense list of components could be slightly reorganized for clarity.
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 complexity of the tool (dependency graph with many features) and the absence of an output schema, the description provides a good overview. It covers the key outputs and options, though it does not detail how to interpret specific analyses (e.g., confidence levels).
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 coverage is 67% (2 of 3 parameters have descriptions). The description adds context for the 'raw' parameter by explaining its effect, but does not elaborate on 'path' (beyond schema) or 'maxFiles'. This adds some value but is not comprehensive.
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 purpose: recursively scan a directory and build a dependency graph. It uses a specific verb ('scan') and resource ('local codebase'), and the listing of components (symbols, call graph, etc.) distinguishes it from sibling tools like export_codebase_graph.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for analyzing code structure but does not provide explicit guidance on when to use this tool versus alternatives like export_codebase_graph or context_stats. It mentions default vs. raw output but lacks when-not or prerequisite conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses auto-escalation behavior, default lossless-ish pipeline, return payload, BPE savings, and reversible ref. Does not cover auth, size limits, or side effects, but provides sufficient behavioral context for safe invocation.
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?
Three sentences, front-loaded with purpose, each sentence adds distinct value. No wasted words.
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 no output schema and three parameters, the description explains the main use case, optional behaviors, and return values. Lacks file type support notes but is sufficient for typical use.
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 coverage is 100% with good parameter descriptions. The tool description adds context about auto-escalation for targetTokens, but largely overlaps with schema. Baseline 3 is appropriate.
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 ('reads and compresses') and the resource ('local file'), and positions it as a shortcut for 'this file is too big to read'. It distinguishes from siblings by focusing on compression, which is not covered by other tool names.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the typical use case (too-big file) and the auto-escalation behavior with targetTokens, but does not explicitly state when NOT to use the tool or mention alternatives among siblings.
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 states the tool is reversible, implying a safe read operation, but does not disclose error behaviors, rate limits, or authentication needs. This is minimally adequate for a simple retrieval 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 a single concise sentence, front-loaded with the action (recover) and key information. Every word contributes value; no wasted text.
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 has one parameter, no output schema, and no annotations, the description sufficiently explains its purpose and relation to other tools. It lacks details on return values or error handling, but for a simple retrieval tool, it is fairly complete.
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 100% for the single parameter 'ref', with a brief schema description. The tool description adds no additional meaning beyond the schema, so the baseline score of 3 is appropriate.
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 recovers the original uncompressed text for a ref returned by prior get_optimized_context or compressed research calls. It specifies the verb (recover), resource (uncompressed text), and source (ref), distinguishing it from siblings like get_optimized_context.
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 indicates when to use the tool—after a prior get_optimized_context or compressed research call—and mentions it's reversible. However, it does not provide explicit when-not-to-use or alternative tools, though context makes it clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses reversibility, preview mode behavior (no ref stored), return values (compressed payload, BPE savings, ref), and use of MCP sampling for summarization. Missing details on error conditions or rate limits, but adequate given complexity.
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 a single dense paragraph, structuring information logically with numbered modes and algorithm list. No wasted words, though could benefit from explicit sections for readability.
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?
Without output schema, description explains return values (compressed payload, BPE savings, ref) and preview mode. Covers all 8 parameters. Lacks explicit error information but is otherwise comprehensive for a complex tool.
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 coverage is 100%, so baseline 3. The description adds value by explaining parameter interactions (e.g., targetTokens auto-escalates, algorithms ignored when targetTokens set, summarize flag behavior) beyond the schema definitions.
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 it is a 'Reversible token-reduction pipeline' that accepts raw text or a file path, distinguishing it from sibling tools like context_stats or retrieve_context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains three modes of use (targetTokens, algorithms, or both) and that exactly one of text or filePath must be provided, but does not explicitly guide when to use this tool versus siblings like crush_file or retrieve_context.
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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