Polymath MCP

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools are clearly distinct by domain (papers, code, models, trends, components, standards). Minor ambiguity exists between search_components and search_trends, as both can surface component-related information, though one focuses on datasheets and the other on discussions.
Naming Consistency5/5
The majority of tools follow a consistent search_<domain> pattern (search_papers, search_code, search_hub, search_trends, search_components, search_standards). The remaining tools use clear verb_noun forms (get_paper, list_sources) that fit naturally without mixing conventions.
Tool Count5/5
With 8 tools, the server is well-scoped for its multi-domain research purpose. Each tool serves a distinct need in the discovery pipeline, and the count is neither too sparse nor overwhelming.
Completeness4/5
The toolset covers the core discovery lifecycle: searching papers, retrieving paper details, searching code, models/datasets, trends, components, and standards. Missing are detail-retrieval tools for non-paper entities (e.g., get_code, get_model), but search results appear to include sufficient information for most use cases.
Average 4.1/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 commits in the last 12 weeks
- Last stable release on August 13, 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.
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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?
No annotations are provided, so the description carries the full burden. It discloses that the tool aggregates discussions from multiple named platforms, but it does not elaborate on result behavior, limitations, or return format. The description is not misleading but leaves behavioral details unmentioned.
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, front-loaded with the core action and resource, followed by a value statement. Every word earns its place with no redundancy or filler.
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 tool has an output schema, so return values are likely covered structurally. However, the description lacks parameter guidance and explicit usage boundaries, making the tool only minimally complete. For a search tool with three parameters and a sibling set, this is adequate but leaves clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description never mentions the parameters (query, limit, sources) or their semantics. With three parameters entirely undocumented, the description fails to compensate for the schema gap, leaving the agent without guidance on how to construct valid requests.
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 function with a specific verb ('Search') and a defined scope ('practitioner discussions and trends on Hacker News, Stack Overflow and the Electronics Stack Exchange'). This distinguishes it from sibling tools like search_papers and search_code, which target different resource types.
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 practical context for when to use the tool: 'Useful as a signal of real-world relevance and for practical questions about a technology.' This gives clear use-case guidance, though it does not explicitly name alternative tools or state 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the behavioral transparency burden. It does not mention whether the search is read-only, requires authentication, has rate limits, or any side effects. It only describes the domain and content, not the behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the action and resource. It uses specific, relevant examples without unnecessary filler.
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 low-complexity search tool with an output schema, the description provides sufficient context about what is searched and for what domain. However, it lacks explicit alternative usage and behavioral details, which is a minor gap.
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 schema has 0% description coverage for parameters, and the description does not explain 'query' or 'limit'. While the parameter names are relatively self-explanatory, the description adds no semantic detail about expected query format or limit behavior.
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 function: 'Search technical standards and specifications' followed by concrete examples like IETF RFCs and Internet-Drafts. It differentiates from siblings such as search_papers and search_code by specifying the standards domain and networking protocols (TCP, HTTP, DNS, TLS, QUIC).
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 context: 'The primary source for networking and internet systems specs.' This indicates it is the go-to tool for networking standards but does not explicitly mention when not to use it or name alternative tools. It falls short of fully explicit guidance.
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 the full burden for behavioral disclosure. It states the basic function and location (GitHub) but does not mention any behavioral traits such as result limits, pagination, or permission requirements. The example adds context but no extra transparency beyond the core operation.
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, front-loaded sentence with no filler. Every word adds value, including the example use case and the optional filter note. Ideal conciseness.
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?
The tool is simple (three parameters) and an output schema exists, so return values do not need explanation. The description covers the essential purpose and the main filter. It loses a point because parameter semantics for query/limit are absent, but overall it is sufficiently complete for a basic search tool.
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%, so the description must compensate. It only clarifies the 'language' parameter ('filter by programming language'), leaving 'query' and 'limit' without additional meaning. The one helpful hint is insufficient for a zero-coverage 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 states a specific verb ('Search') and resource ('code repositories on GitHub'), with a concrete example of use (finding implementations of papers/libraries). This clearly distinguishes it from sibling tools like search_papers or search_trends, which target other entity types.
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 context for when to use the tool ('to find the implementation of a paper or a library') and mentions an optional filter (programming language). However, it does not explicitly name alternatives or state when not to use this tool, so it falls short of a full 5.
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 the burden of behavioral disclosure. It adds useful context about results including 'linked arXiv ids when available,' which is a behavioral trait beyond the basic search action. However, it does not mention other behaviors like sorting, pagination, or that it is a read-only operation, though 'Search' implicitly suggests read-only.
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, front-loaded with the main purpose. The second sentence adds a valuable detail about arXiv ids without extra fluff. Every word earns its place, and it is appropriately concise.
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, the description covers the core behavior and scope. An output schema exists, so return values are already documented. The description explains the 'kind' parameter and the arXiv linking behavior, which are the non-obvious aspects. No significant gaps remain for a straightforward search tool.
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 0%, so the description must compensate. It explains the 'kind' parameter with valid values ('models' or 'datasets'), adding meaning beyond the schema. The 'query' and 'limit' parameters are left implicit, but their purposes are reasonably obvious from their names and types, so the compensation is partial.
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 function: 'Search the Hugging Face Hub for models or datasets.' This uses a specific verb and resource, and distinguishes from sibling tools like search_papers and search_code by focusing on models/datasets. It also adds a unique detail about linking arXiv ids.
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 context for when to use the tool: 'for models or datasets,' and explains the 'kind' parameter to select one or the other. It doesn't explicitly exclude alternatives or mention sibling tools, but the scope is clearly implied, giving no confusion about its intended use.
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 responsibility for disclosing behavior. It states that it lists all sources and their categories, but does not mention potential limitations, whether it is read-only, or how the data is returned. For a simple list operation this may be adequate, but more detail would be helpful.
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 that communicates the purpose and scope without any filler. It is front-loaded with the action verb and clearly 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?
Given the zero parameters and the existence of an output schema (which the description need not explain), this one-sentence description is complete. It fully explains what the tool does and is appropriate for the simple complexity of this listing operation.
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 tool has zero parameters, so the input schema is empty. The description appropriately focuses on the action and result rather than parameter details. Since there are no parameters, there is nothing more to explain, and the baseline 4 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?
The description uses a specific verb ('List') and clearly identifies the resource ('every available data source') and the scope (including the category it serves). This distinguishes it from the sibling search tools, which are for finding specific papers, code, trends, etc., not for enumerating all sources.
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 the usage context: when you need to enumerate all available data sources rather than search for specific content. However, it does not explicitly state when not to use it or mention alternatives, but the sibling tools are distinct enough that the intended use is 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?
With no annotations, the description carries the burden. It explains the tool queries keyless sources, clarifies scope (existence/canonical naming), and acknowledges partial coverage. However, it omits details like result format or pagination, though an output schema exists.
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, front-loaded with the primary action ('Search electronic component information'), and contains no filler or redundant information.
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 output schema and simple parameters, the description provides sufficient context about purpose and limitations, but the lack of parameter guidance slightly reduces completeness.
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 schema documents query (string, required) and limit (integer, default 10) but with 0% description coverage. The description does not elaborate on acceptable query formats, how limit behaves, or what constitutes a good query, so it fails to compensate for the schema's lack of 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 opens with a specific verb+resource ('Search electronic component information') and names two concrete source types (Internet Archive databooks, KiCad symbol libraries), which clearly distinguishes it from sibling search tools like search_papers and search_code.
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?
It explicitly states a coverage limitation ('No keyless commercial parts API exists, so coverage is partial') and directs users to an alternative for practical discussion ('for practical component discussion also try search_trends'), which is strong when-to-use vs. alternative 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?
No annotations are provided, so the description carries the burden. It discloses deduplication logic (DOI/arXiv id) and sorting by citation count, which are behavioral traits beyond a generic search description. It does not mention rate limits or auth, but for a read-only search tool this is acceptable.
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 that front-load the core purpose and add key behaviors (dedup, sorting) without wasting words. Every sentence earns its place.
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?
The tool is moderately complex with 5 parameters and an output schema. The description covers the main search behavior, deduplication, sorting, and filtering, which is enough for an agent to invoke it correctly. The output schema handles return structure, so no additional description is needed.
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 schema has no property descriptions, so the description must compensate. It explains the sources and year range filters ('Optionally restrict to specific sources or a year range'), but does not clarify the 'limit' or 'query' parameters. This is partial compensation, leaving some reliance on self-explanatory parameter names.
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 uses a specific verb ('Search') and resource ('academic papers'), enumerates the exact sources, and distinguishes from sibling tools like search_code and search_standards. It also adds deduplication and sorting details, making the tool's purpose unambiguous.
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 clearly states the tool searches academic papers and notes optional source/year filters, implying use for scholarly literature queries. There are no explicit exclusions or alternatives, but the context is clear enough for an agent to decide when 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 provided, the description carries the full burden. It discloses key behaviors: merging metadata from all known sources and including an open-access PDF link when available. It doesn't mention error handling or response formats, but for a read operation this is sufficient context.
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 verb and resource. Every word earns its place—no filler, no repetition of schema details, and the identifier examples are compact and illustrative.
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?
The tool is simple (one parameter, one paper lookup). The description explains what to pass and what the result contains (merged metadata, PDF link). With an output schema present, this is complete for the tool's scope.
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 fully compensates by explaining the 'identifier' parameter accepts DOI or arXiv ID with concrete examples showing accepted formats. This is far more informative than a generic 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 function: 'Get details for one paper by DOI or arXiv id'. It specifies the resource (paper), the action (get details), and the identifier types, distinguishing it from sibling search tools.
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 when to use this tool: when you have a specific DOI or arXiv ID. It contrasts with sibling search tools by focusing on direct identifier lookup, though it doesn't explicitly name alternatives or exclusions.
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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