mcp-research

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Server Quality Checklist

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Latest release: v0.3.0
Disambiguation5/5
Each tool targets a distinct source or operation: academic references, local files, URLs, compound research, Twitter, vault status, web search, and YouTube. There is no ambiguity between tools.
Naming Consistency2/5
Tool names use mixed conventions: verb_noun (fetch_url, web_search), noun_noun (vault_status, youtube_essence), platform_verb (twitter_extract), and single word (research). No consistent pattern.
Tool Count5/5
8 tools is an appropriate scope for a research assistant, covering key sources (web, academic, social media, local files) without being overwhelming.
Completeness4/5
The toolset covers major research workflows: search, fetch, extract, and synthesize. Minor gaps like result organization or citation management are not critical for core functionality.
Average 4.5/5 across 8 of 8 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
- Last stable release on April 9, 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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{
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}
```
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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?
Annotations already convey read-only, idempotent, and non-destructive behavior. The description adds that it supports multiple access methods, which is useful context beyond annotations, but doesn't detail error handling or rate limits.
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 very concise: two sentences for purpose and two bullet-point args. No wasted words, front-loaded with main 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 the tool has only 2 simple params and an output schema (not shown), the description covers the essential behavior and parameter semantics. It's mostly complete, though could mention output format briefly, but output schema covers that.
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 0% schema description coverage, the description fully compensates by explaining the url format (x.com/user/status/id) and the purpose of include_thread (fetch full thread). Both parameters are clearly described.
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 'Extract' and the resource 'tweet or thread from X.com/Twitter', distinguishing it from siblings like fetch_url by being Twitter-specific.
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 provides technical details (yt-dlp, API, cookie-based access) but lacks explicit guidance on when to use this tool versus alternatives like fetch_url. No when-not-to-use or exclusion criteria.
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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds no behavioral traits beyond these, such as external API dependency or rate limits. Despite annotations covering safety, the description misses contextual details like needing internet access.
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 very concise: a single sentence defining purpose followed by a well-structured Args list. Every sentence is meaningful, and the structure is front-loaded with the core action.
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's simplicity (2 parameters, no nested objects), the description covers purpose, parameters, and output types. It lacks information on error handling or return format, but the existence of an output schema mitigates this. Overall, it is adequately complete.
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 0% schema description coverage, the description fully compensates by explaining the 'url' parameter with allowed formats and the 'mode' parameter with three depth levels and their effects. This adds significant meaning beyond the raw 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 action ('Extract essence') and the resource ('YouTube video'), followed by a list of outputs (transcript, summary, key points, chapters, quotes). This distinguishes it from siblings like twitter_extract or fetch_url which target different sources or actions.
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 clear usage context via parameter explanations (allowed URL formats and mode options). However, it does not explicitly mention when to use this tool over alternatives or exclude scenarios, though the specificity to YouTube serves as implicit guidance.
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?
The description adds behavioral context beyond annotations: it mentions attempting to fetch full text via vault credentials/EZproxy, which is a key side effect. Annotations already declare readOnlyHint=true and idempotentHint=true, so there is no contradiction. The description supplements annotations well.
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 concise and front-loaded with the primary purpose, followed by parameter details. It contains no extraneous text. Slightly more structure (e.g., separating args clearly) could improve scannability, but it is already 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?
Given the presence of an output schema, the description appropriately focuses on input behavior. It covers the main use cases and mentions the vault configuration requirement. Minor gaps exist (e.g., what happens if fetch_fulltext fails), but overall it is sufficiently complete for a well-annotated 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?
The input schema has 0% description coverage, so the description must compensate. It explains that 'identifier' can be a DOI, ArXiv ID, PubMed ID, or publisher URL, and that 'fetch_fulltext' defaults to true. This provides necessary semantics that the schema alone lacks.
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 resolves specific academic identifiers (DOI, ArXiv ID, PubMed ID) and optionally fetches full text via institutional access. The verb 'Resolve' and listing of identifier types provide a specific purpose that distinguishes it from siblings like web_search and fetch_url.
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?
It explicitly states when to use the tool (for resolving academic identifiers and fetching papers with vault access). While it does not provide explicit 'when not to use' guidance, the sibling tools offer natural alternatives, and the context is clear enough for an AI agent to infer appropriate usage.
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?
Description adds SSRF protection, caching, and conditional summarization beyond annotations' readOnly/idempotent hints. No contradictions. More details on error handling would improve, but current info is solid.
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?
Extremely concise: single opening sentence plus a three-line bullet list. No fluff, every sentence adds value. Perfect structure for quick scanning.
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 output schema exists, description doesn't need return details. It covers security (SSRF), caching, and parameter nuances. Missing authentication or error info, but overall adequate for a fetch tool.
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 0% schema description coverage, the description fully explains each parameter: url is the URL, summarize has Ollama condition, max_chars includes default and advice to increase for full pages. Adds significant value 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 'Fetch a URL, convert to markdown' with specific verb and resource. It distinguishes from siblings like web_search and academic_lookup by focusing on fetching a single URL rather than searching or academic data.
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 implies usage: fetch a specific URL for markdown conversion. It doesn't explicitly compare to siblings but provides enough context (e.g., Ollama availability for summarization) to guide appropriate use.
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?
Annotations confirm read-only, idempotent, non-destructive behavior. The description adds value by detailing the extraction process (text from various formats) and the optional AI summarization feature, which annotations do not cover.
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 concise: a one-line overview followed by a clean bullet-style Args section. Each sentence serves a purpose, and the essential information is 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 complexity (many file types, 4 parameters, optional summarize), the description sufficiently covers purpose, parameters, and behavior. An output schema exists, so return values need not be detailed.
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 description coverage is 0%, but the description provides detailed parameter docs (path, include_types, max_files, summarize) with defaults and examples (e.g., 'Comma-separated type filter... Empty = all'). This fully compensates for the schema gap.
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 that the tool extracts text from files (directories or single files), listing supported formats (text, PDF, DOCX, etc.). This distinguishes it from sibling tools like fetch_url (URLs), web_search (web queries), and youtube_essence (YouTube).
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 explains the tool's scope (local file processing) and supported types, providing clear context. However, it does not explicitly state when not to use it or mention alternatives beyond implied differences from siblings.
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?
Annotations already declare readOnlyHint=true and idempotentHint=true. Description adds security assurance 'Never exposes secrets', which is valuable beyond annotations. No contradiction.
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 purpose, second adds critical security note. Efficient and well-structured.
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?
Zero parameters, good annotations, output schema exists. Description fully covers the tool's behavior and safety. No gaps.
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?
No parameters, schema coverage 100%. Description adds meaning by specifying what the tool shows (status, profiles, dependencies) 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?
Description uses specific verb 'Show' and resource 'credential vault status', with clear scope including loaded profiles and dependency availability. Distinguishes from siblings by being the only vault-related tool.
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?
Usage context is clear: a status tool to check vault state. No explicit alternatives or exclusions, but the purpose implies when to use. Slight lack of when-not guidance.
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?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable transparency by revealing the 3-tier cascade (Brave, DuckDuckGo, scraper) and optional Ollama summarization, which are beyond what annotations provide.
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 succinct, uses a bullet list for arguments, and front-loads the cascade mechanism. Every sentence provides 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 presence of an output schema (not shown but indicated), the description adequately covers input parameters and behavior. It is complete for an AI agent to invoke the tool correctly.
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 description coverage is 0%, so the description must explain parameters. It does so effectively: query as search string, max_results (1-20), summarize (if Ollama available), and auto_fetch_top (fetch top result content). This adds substantial meaning beyond the JSON 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 it searches the web using a 3-tier cascade, differentiating it from siblings like academic_lookup, fetch_url, and research. The verb 'Search' and resource 'web' are specific, and the cascade detail adds precision.
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?
While the purpose is clear, the description does not provide explicit guidance on when to use this tool versus alternatives like fetch_url or research. It lacks when-not conditions or comparative context.
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?
Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: the multi-step process (search, fetch, summarize, synthesize) and the meaning of depth, which goes beyond the 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 very concise with a front-loaded pipeline overview and bullet points for arguments. Every sentence adds value; no wasted words.
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 that there is an output schema (not shown) and annotations cover safety, the description explains the tool's composite nature, parameter meanings, and pipeline stages. It is complete for an agent to understand and invoke the tool correctly.
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 description coverage is 0%, so the description carries full burden. It explains all three parameters: query is the research question, depth with three options, and context as optional prior research. This fully compensates for missing schema descriptions.
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 does 'Compound research: search → fetch top pages → summarize → synthesize', which is a specific verb+resource and distinguishes it from sibling tools like web_search, fetch_url, or academic_lookup that perform only individual steps.
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 the research pipeline and explains the depth parameter with clear options. It implies use for comprehensive research combining multiple steps, but does not explicitly state when not to use or compare directly with siblings.
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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