solidit-mcp-server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: search_findings for complex queries, get_finding_detail for full content retrieval, search_by_tag for pattern lookup, and recent_findings for browsing. No overlap or ambiguity exists.
Naming Consistency4/5Tool names follow a consistent snake_case pattern with clear verb_noun structure (search_findings, get_finding_detail, search_by_tag, recent_findings). However, 'recent_findings' uses an adjective instead of a verb, slightly breaking the pattern.
Tool Count5/5With 4 tools, the set is well-scoped for querying a blockchain audit finding database. Each tool serves a distinct access pattern—search, detail, tag-based lookup, and recency browsing—without unnecessary bloat.
Completeness4/5The tool surface covers the main ways to discover and retrieve findings: search, detail, tag, and recent. A minor gap is the lack of bulk export or comparison tools, but the core functionality is complete for an audit database query service.
Average 4.5/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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 MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
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To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses sorting behavior ('sorted by recency (newest first)'), default time range (30 days), and available filters (impact, language, protocol category). It does not mention pagination details or rate limits, but the schema covers page/page_size. The description is transparent about the core behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a single-sentence summary, followed by a bullet list of when-to-use scenarios, and then a clear list of alternatives. Every sentence contributes value, and the structure is front-loaded with the most important information. No redundant or verbose content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 6 parameters (all optional) and no output schema. The description covers the main intent (browsing recent findings with filters) and specifies sorting and default time range. It does not describe the output format or pagination behavior, but the schema provides page/page_size defaults. For a browse tool, the description is fairly complete, though a note about the structure of returned findings would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds minimal new meaning beyond the schema: it mentions 'optimized presets for 30/60/90 days' for the 'days' parameter, and lists the filter categories (impact, language, protocol category) which are already detailed in the schema. The description does not significantly enhance parameter understanding beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Browse the most recent Solodit findings from the last N days.' It specifies the verb (browse), resource (findings), and scope (recent, last N days, sorted by recency). It also distinguishes itself from siblings by naming alternative tools with different use cases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit 'When to use' scenarios (checking new vulnerabilities, staying up to date, browsing with filters) and explicitly lists when NOT to use the tool, directing to search_findings, search_by_tag, and get_finding_detail for specific alternatives. This is excellent guidance for an AI agent.
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 the full burden. It states that parameters are optional, a bare call returns recent findings, and returns structured results. It does not explicitly state read-only behavior, side effects, rate limits, or performance characteristics. While safe for a search tool, more disclosure would improve transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: a concise overview, then a bulleted list of siblings with guidance, then 'When to use', then a complete list of filters. Every sentence adds value, and the most important information (purpose, differentiation) is front-loaded. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (17 parameters, no output schema, no annotations), the description covers purpose, usage guidance, filter details, and return structure. It is nearly complete, but could mention behavioral aspects like read-only nature or any rate limits to be fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds value by listing all filters with examples, enumerating top tags, all firms, and categories, and providing context like 'example values'. This goes beyond the schema descriptions, aiding the agent in understanding parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search' and the resource 'Solodit's database of blockchain security audit findings'. It distinguishes itself from sibling tools by positioning as the most powerful with 14+ filters, and mentions specific alternatives for simpler queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use: 'complex multi-filter queries... that the convenience tools don't support'. It also provides clear alternatives: get_finding_detail, search_by_tag, recent_findings, with brief descriptions of each. This is excellent guidance for the agent.
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 description carries full burden. It discloses sorting by quality score, that results are best examples first, and that tags use exact casing. However, it doesn't explain pagination behavior or what happens with invalid tags. A 4 is generous but justified given the clear behavioral claim about optimization and sorting.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by sorting behavior. Usage guidelines are clearly sectioned. The tag lists are long but justified by the necessity for exact casing. One sentence could be trimmed ('results are sorted by quality score' is already implied by 'optimized for finding examples'). Still, overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 6 parameters and no output schema, the description is complete. It covers all key aspects: what the tool does, when to use it, behavior (sorting), tag usage guidance, and clear sibling differentiation. The lack of output schema is mitigated because the purpose is clearly retrieval of findings examples, and the schema path explains parameters fully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, but the description adds value by listing commonly used tags in an organized manner and repeating them for easy copy-paste. It also provides the exact casing requirement which is critical for correct usage. The 'max: 100' and 'defaults' are already in schema, but the human-readable list of top tags adds semantic value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it searches Solodit findings by vulnerability tags, specific verb 'search' + resource 'findings' + qualifier 'by vulnerability tags'. It distinguishes itself from siblings by mentioning it's optimized for specific vulnerability pattern examples with quality sorting, while sibling 'search_findings' is for complex multi-filter queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit 'When to use' bullet points with three concrete scenarios. Directly names alternatives: 'Use search_findings for complex multi-filter queries', 'Use get_finding_detail to read full content', and 'Use recent_findings to browse'. This gives clear guidance on when not to use this tool.
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 are provided, so the description must carry the full burden. It discloses that the tool returns 'complete untruncated content, all tags, finders, contest info, and links,' which implies a read-only behavior. However, it does not explicitly state that it is non-destructive, has no side effects, or mention any authentication or rate limit requirements. The transparency is good but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: a clear purpose sentence, a 'when to use' paragraph, a bulleted list of use cases, and a distinct section for alternatives. Every sentence is informative and earns its place; no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter retrieval tool with no output schema, the description covers all necessary aspects: what it returns, when to use it, and which sibling tools to use instead. The agent can confidently decide when and how to invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (one parameter fully described in the schema). The description adds value by connecting the parameter to its source ('Found in search_findings results or Solodit URLs'), which aids the agent in constructing the correct input. This goes beyond the schema's own description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Retrieve') and resource ('full content and all metadata of a single Solodit finding by its ID or slug'). It clearly distinguishes from siblings by outlining when to use this tool versus search_findings, search_by_tag, and recent_findings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use the tool ('when you already know which finding you want to examine in detail'), provides three bullet points of use cases, and lists three sibling tools to use instead under different conditions. This is exemplary guidance.
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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- Evaluate tool definition quality.
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