HydraMCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool serves a clearly distinct purpose: analyze_file for file analysis, smart_read for code extraction, ask_model for single model queries, compare_models for side-by-side comparison, consensus for voting, synthesize for merging responses, list_models for discovery, and session_recap for session history. No two tools overlap in function.
Naming Consistency4/5
Most tools use verb_noun pattern (analyze_file, ask_model, compare_models, list_models, session_recap, smart_read) but 'consensus' and 'synthesize' are single verbs without nouns, breaking the pattern. This minor inconsistency does not hinder readability.
Tool Count5/5
With 8 tools, the server covers the core capabilities of model interaction, file analysis, and session retrieval without being overly broad. Each tool seems necessary and the count feels well-scoped for the stated domain.
Completeness4/5
The tool surface covers the main workflows: file analysis (analyze_file, smart_read), model queries (ask_model, compare_models, consensus, synthesize), and context restoration (session_recap). Missing are tools for managing model configurations, provider settings, or cache control, which would add polish but are not essential.
Average 4.3/5 across 8 of 8 tools scored. Lowest: 3.3/5.
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
- Last stable release on February 7, 2026
- 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.
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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 burden of disclosing behavior. It reveals parallel execution and combination of best ideas, but omits details on failure handling, timeouts, or the subjective 'better than any single model' claim. Additional context on potential issues 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loads the key action (parallel query and synthesis), and contains no extraneous information. Every word serves a purpose, making it highly efficient.
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 tool with 6 parameters and no output schema, the description is moderately complete. It covers the core functionality and result, but lacks details on parameter usage, return format, and edge cases. Additional information would be beneficial given the tool's complexity.
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 description adds no information about parameters; it solely describes the tool's operation. With 50% schema coverage (only 3 of 6 parameters have descriptions), the description fails to compensate for the gap, leaving agents to rely on parameter names alone for meaning.
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?
The description clearly states the tool queries 2-5 models in parallel and combines their ideas into a single answer. It distinguishes itself from siblings like 'ask_model' (single model) and 'compare_models' (comparison, not synthesis), but lacks explicit differentiation from 'consensus' which may have similar purpose.
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 when a synthesized answer better than any single model is desired, but provides no when-not-to-use guidance or alternatives. Sibling tools like 'compare_models' or 'consensus' are not mentioned, leaving the agent to infer the best tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only says it queries and aggregates. Does not disclose performance implications, rate limits, or failure modes (e.g., no consensus).
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?
Single sentence conveying essence with no wasted words. Efficient and front-loaded.
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?
With 7 parameters, no output schema, and no annotations, the description is too minimal. Lacks details on return format, error handling, and default behaviors.
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 57%, description adds context for strategy (enum values) but does not elaborate on system_prompt, temperature, or max_tokens beyond 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?
Description clearly states the action (query and aggregate), resource (models), and result (consensus answer with confidence). Distinguishes from siblings like 'ask_model' and 'compare_models'.
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?
Implies use for consensus among models, but lacks explicit when-not-to-use or alternative comparisons. Context is clear but not directive.
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 carries full burden. It discloses parallel execution and return metrics, but lacks details on failure handling, rate limits, or latency implications. Basic transparency 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?
Single sentence, front-loaded with key information, no unnecessary words. Highly efficient.
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 tool with 6 parameters and no output schema, the description is minimal. It does not detail return format or error cases. Adequate for basic understanding but not fully 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 coverage is 67%, and description adds high-level context of parallel execution and return metrics. However, it does not explain semantics of parameters like temperature or max_tokens beyond schema. Adequate but not exceptional.
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 action (query multiple models in parallel), the resource (models), and the result (side-by-side comparison with latency and token metrics). It effectively distinguishes from siblings like ask_model.
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?
Usage context is implied but no explicit when-to-use or when-not-to-use guidance, nor comparison to siblings. The description does not mention alternatives like ask_model for single model queries.
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, but the description indicates a simple read-only operation with no side effects. It doesn't delve into authentication or data freshness, but for a list tool with no parameters, the behavioral disclosure is adequate.
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 short, direct sentences with no unnecessary words. Every piece of information is valuable and front-loaded.
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 simplicity (no parameters, no output schema), the description fully covers what it does and when to use it. No missing information for an agent to correctly invoke it.
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 in the schema, so the description doesn't need to add param-level details. It exceeds the baseline by noting the scope ('across all providers'), which adds context 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 all available models across all providers, with a specific verb and resource. It also suggests a use case ('Run this first to see what you can query'), distinguishing it from sibling tools that query or compare specific models.
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?
Implicitly recommends running this tool first before using other model-related tools (like ask_model, compare_models). While it doesn't explicitly state when not to use it or mention alternatives, the context is clear enough for an agent to understand its role as a discovery 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 provided, so description carries full burden. It discloses that Claude never sees raw data, details output sections, and enumerates failure modes. Lacks explicit statement that it is a read-only operation, but this is implied by the purpose.
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?
Well-structured with clear sections (OUTPUT, WHEN TO USE, FAILURE MODES). Slightly verbose due to extensive failure mode details, but each sentence adds value. Front-loaded with essential purpose.
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 no annotations, the description covers purpose, usage, output format, and failure modes comprehensively. It could potentially include more on return value structure, but the markdown format is sufficiently described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds significant value beyond schema: clarifies auto-detection for project, auto-calculation for max_summary_tokens, and purpose of focus. Provides concrete examples (e.g., 'auth implementation').
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 reads previous sessions and generates a recap. The verb 'Read' and 'generate' along with the resource 'previous Claude Code sessions' precisely define the tool's action, distinguishing it from sibling tools like analyze_file or ask_model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Includes an explicit 'WHEN TO USE' section with clear scenarios (start of session, restore context, continue work). Also provides failure modes with retry actions, which guide the agent on next steps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description thoroughly discloses behavioral traits: compression and distillation details, caching indicators ('(cached)'), failure modes including circuit breaker and model unavailability, and edge cases like compression silently skipped. This compensates fully for the lack of annotations.
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 efficiently structured with clear sections (OUTPUT, WHEN TO USE, FAILURE MODES), no redundant sentences, and front-loaded with the core functionality. Every sentence adds value.
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 has 8 parameters and no output schema or annotations, the description covers expected behavior, output format, parameter usage, failure modes, and edge cases comprehensively. It leaves little ambiguity and provides sufficient context for correct tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description adds significant value by explaining the compression mechanism for max_response_tokens, the verification purpose of include_raw, and the response metadata (latency, token usage, distillation info). It enhances parameter understanding beyond the 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's purpose: 'Query any AI model with a prompt. Returns the model's response with metadata.' It uses a specific verb and resource, and the output format distinguishes it from sibling tools like list_models or compare_models.
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 includes a 'WHEN TO USE' section explaining the appropriate context ('When you need another model's perspective, analysis, or capabilities') and provides guidance on parameters like max_response_tokens and include_raw. However, it does not explicitly mention when not to use this tool or compare it to alternatives like consensus.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description fully discloses behavior: file read server-side, no context window, output includes metadata, compression via max_response_tokens with distillation, and failure modes. Contradicts no structured data.
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?
Description is well-structured: overview, output format, when to use, failure modes. Front-loaded with purpose. Each sentence adds value, no waste. Appropriate length for a complex tool.
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?
For a tool with 7 parameters and no output schema, the description covers purpose, behavior, output format (Markdown with metadata), compression, and failure modes. Complete enough for an agent to use correctly.
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 100%, so baseline is 3. The description adds extra meaning: file_path must be absolute, prompt should be specific, model auto-picks, max_response_tokens triggers distillation, max_tokens default, format enum, include_raw for verification. This goes beyond 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's purpose: offload file analysis to a worker model, keeping the file out of the context window. It uses specific verbs (analyze, review, search) and resources (file, file path, question), distinguishing it from siblings like ask_model or smart_read.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit 'WHEN TO USE' section advises using the tool to avoid reading files, especially large ones. Failure modes explain when to retry (file not found), when not to retry (binary file), and when to ask user (file too large). Provides clear context and exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses behavior: it reads files server-side without entering context, lists output format (Markdown with line numbers), includes failure modes with explanations, and notes autoselection of large-context model. No contradictions.
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 well-structured with clear sections: purpose, output format, when to use, and failure modes. It is front-loaded with the core verb and differentiator, and every sentence adds value without redundancy.
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 (7 parameters, no output schema, multiple failure modes), the description is remarkably complete. It covers output format, parameter behaviors, failure modes, and usage context. All necessary information for correct invocation is present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the description adds significant value beyond the schema. For example, it explains the auto-pick behavior for the 'model' parameter, distillation behavior for 'max_response_tokens', and the purpose of 'include_raw' for quality comparison.
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: 'Surgical code extraction from files' returning only relevant code sections with line numbers. It explicitly differentiates from the sibling tool 'analyze_file' by stating it returns actual code not prose analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'WHEN TO USE' section provides explicit guidance: use instead of the Read tool for specific intents, especially for large files. It also specifies when not to use: for general questions, use 'analyze_file' instead. This covers usage context and alternatives.
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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