TensorFeed
Server Details
AI news, model pricing, service status, and machine-payable premium tools. AFTA-certified.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- RipperMercs/tensorfeed
- GitHub Stars
- 1
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4/5 across 18 of 18 tools scored. Lowest: 3.1/5.
Each tool targets a distinct external API or dataset (FDA, SEC, CVE, EPSS, KEV, OSV, energy, AI news, model pricing, status, opportunity scanning). Even within the same domain (e.g., FDA tools), each has a clear, non-overlapping purpose (adverse events, labels, recalls, device events, food recalls). No ambiguity.
Naming conventions are inconsistent: most FDA tools use query_ (e.g., query_fda_drug_events), many other tools use get_ (e.g., get_cve_record), while SEC tools mix get_sec_submissions, lookup_sec_company_ticker, and search_sec_edgar. This mixed verb pattern (query/get/lookup/search) makes it harder for an agent to predict tool names.
With 18 tools, the count is on the high side but each tool serves a different API. However, the scope is extremely broad (AI, cybersecurity, energy, finance, healthcare), making the server feel like a collection of unrelated data sources rather than a focused toolset. A more focused server would have fewer tools.
For a read-only data aggregation server, the coverage within each domain is good: FDA safety data (5 tools), SEC filings (3 tools), cybersecurity vulnerabilities (5 tools), AI ecosystem (4 tools). Minor gaps exist (e.g., no energy data beyond EIA series, no FDA inspections), but no critical operations are missing for typical use cases.
Available Tools
23 toolsget_agent_opportunitiesAInspect
Get TensorFeed's daily scan of new repositories across the AI agent ecosystem (Anthropic, OpenAI, Microsoft, ModelContextProtocol, HuggingFace, LangChain, frontier labs) plus recent MCP/x402/skills keyword sweeps. Each opportunity includes the GitHub repo path, description, stars, last update, the source signal, and a composite score (signal weight × log10(stars+1) × recency decay). Refreshed daily at 13:30 UTC. Useful for surfacing distribution targets, integration ideas, or just a daily digest of what's launching across the agent space. License: GitHub data via the public Search API; output is TensorFeed's curated ranking.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max opportunities to return (1-25) | |
| signal | No | Optional filter to one signal source. One of: anthropic-org, openai-org, microsoft-org, mcp-org, huggingface-org, langchain-org, frontier-labs, mcp-keyword, x402-keyword, skill-keyword, vertical-pattern. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses daily refresh at 13:30 UTC, composite score formula, and source signals. It does not mention any side effects or destructive behavior, which is appropriate for a read-only tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is 4-5 sentences, front-loads the main purpose, and efficiently covers content, scoring, and use cases without extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains return fields, scoring, and update schedule. It could specify default ordering (likely by composite score descending) but is otherwise complete for a list endpoint.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the two parameters. The description repeats the parameter meanings (limit bounds, signal options) but adds no new semantics beyond what is in the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'TensorFeed's daily scan of new repositories across the AI agent ecosystem', listing specific sources and what each opportunity includes. It is distinct from sibling tools which target security, finance, and FDA data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly mentions use cases: 'surfacing distribution targets, integration ideas, or just a daily digest'. It does not provide when-not-to-use scenarios, but the tool is unique among siblings, so no exclusion is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_ai_trending_papersAInspect
Get the daily curated AI/ML trending papers from Semantic Scholar, ranked by citation count. Five fan-out queries (large language model, transformer, RLHF, AI agents, diffusion model), deduped by paperId, top 30 returned. Each entry carries paperId, title, abstract, authors, year, venue, citationCount, arxivId, doi, and fieldsOfStudy. Refreshed daily at 11:00 UTC. Citation-ranked counterpart to get_arxiv_recent (firehose by submission date). License: Semantic Scholar API permits use; the standard attribution block ships on every response.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max papers to return (1-30). Default 15. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden. It discloses the five fan-out queries, dedup by paperId, top 30 return limit, refresh schedule (daily at 11:00 UTC), license terms, and attribution requirement. No contradictions or omissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with purpose first, then technical details. Each sentence adds value, though slightly verbose. Could be trimmed slightly but still effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description lists all returned fields (paperId, title, abstract, etc.). Covers refresh schedule, licensing, and attribution. Fully addresses complexity for a list retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one parameter limit having a clear description. The description adds context (top 30, default 15) but does not significantly extend beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool gets daily curated AI/ML trending papers from Semantic Scholar, ranked by citation count. Provides specific details on fan-out queries and dedup logic. Explicitly distinguishes from sibling tool get_arxiv_recent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly positions the tool as the citation-ranked counterpart to get_arxiv_recent, giving clear context on when to use each. Mentions licensing and attribution, but does not explicitly state when not to use or provide alternatives beyond the one sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_arxiv_recentAInspect
Get the 50 most recent arXiv submissions in cs.AI / cs.LG / cs.CL / cs.CV, sorted by submission date. Each entry carries arxivId (no version suffix), version, title, abstract, authors, primary category, all categories, publishedAt, updatedAt, htmlUrl, pdfUrl, and doi. Refreshed daily at 11:30 UTC. The firehose pair to get_ai_trending_papers (which ranks by citation count). License: arXiv permits use of metadata; the standard attribution block ships on every response.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max papers to return (1-50). Default 25. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses return fields, categories, refresh schedule, and license. It is clearly a read operation, but could explicitly state it requires no authentication or mention 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no fluff. The purpose is front-loaded, and every sentence provides essential information: scope, fields, refresh, sibling pair, license.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with one optional parameter and no output schema, the description covers categories, fields, refresh schedule, and licensing. No critical gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with the limit parameter described. The description adds context that the maximum is 50 (implied by '50 most recent') but does not significantly enhance the schema's meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves the 50 most recent arXiv submissions in specific categories, sorted by date. It distinguishes itself from the sibling get_ai_trending_papers which ranks by citation count.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly mentions the sibling tool get_ai_trending_papers and contrasts it (firehose vs ranking by citation). Also notes that the data is refreshed daily at 11:30 UTC, providing context on when to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_cve_recordAInspect
Look up a single CVE Record v5.2 from the MITRE CVE List by ID (e.g. CVE-2024-3094). Lazy-fetched and cached 7 days. License: MITRE CVE Terms of Use, commercial redistribution permitted; the response includes the standard attribution block.
| Name | Required | Description | Default |
|---|---|---|---|
| cve_id | Yes | CVE identifier in CVE-YYYY-NNNNN form, case-insensitive |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses lazy-fetching, 7-day caching, and licensing terms (MITRE CVE Terms of Use, commercial redistribution permitted). However, it does not explicitly state that the operation is read-only or mention rate limits or latency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences are each substantive and non-redundant: purpose, caching behavior, and licensing/attribution. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter lookup tool with no output schema, the description covers purpose, caching, and licensing. It mentions the response includes an attribution block but does not describe the full return structure, which would be helpful since no output schema is provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a good description of the parameter 'cve_id' (CVE identifier in CVE-YYYY-NNNNN form, case-insensitive). The description's mention of the parameter adds no new information beyond the schema, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool looks up a single CVE Record v5.2 from the MITRE CVE List by ID, using a specific example. It distinguishes from sibling tools which cover different data sources (e.g., EPSS, OSV, FDA, SEC).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for single CVE lookups ('Look up a single CVE Record'), but does not explicitly mention when not to use it or compare to siblings like get_epss_score or get_osv_advisory_by_id. It provides clear context but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_eia_seriesAInspect
Get a US Energy Information Administration time-series. Curated routes: petroleum/pri/spt (WTI/Brent crude spot prices), petroleum/pri/gnd (US retail gasoline), natural-gas/pri/sum (Henry Hub + retail nat gas), electricity/retail-sales (by state and sector), electricity/electric-power-operational-data (net generation by fuel), total-energy. License: US Government public domain.
| Name | Required | Description | Default |
|---|---|---|---|
| end | No | End date YYYY-MM-DD | |
| route | Yes | One of: petroleum/pri/spt, petroleum/pri/gnd, natural-gas/pri/sum, electricity/retail-sales, electricity/electric-power-operational-data, total-energy | |
| start | No | Start date YYYY-MM-DD | |
| length | No | Max records (1-5000) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It indicates a read operation but does not discuss any behavioral details such as rate limits, authentication, error handling, or the nature of the returned data beyond 'time-series'. It does not contradict annotations (none present).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences. The first sentence states the core purpose, and the second lists routes and license. It could be improved by using a bullet list for the routes, but it is clear and not verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains what the tool does and lists all allowed routes with examples, which is good. However, it lacks details on the return format, pagination, or error conditions. With no output schema, the description should cover more of the behavioral context to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers all parameters at 100%. The description adds significant value by listing the allowed routes with explanatory examples (e.g., 'WTI/Brent crude spot prices' for petroleum/pri/spt), which goes beyond the simple enum in the schema. However, it does not add to start, end, or length parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb ('Get') and the resource ('US Energy Information Administration time-series'), and lists specific curated routes. It clearly distinguishes this tool from sibling tools which cover unrelated topics like CVEs, EPSS, and FDA data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for retrieving EIA time-series data, but does not explicitly state when to use it versus alternatives or provide any usage context or exclusions. The sibling tools are unrelated, so the purpose is clear, but no explicit guidelines are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_epss_scoreAInspect
Get the EPSS (Exploit Prediction Scoring System) probability for one CVE, sourced from FIRST.org. Returns the daily probability (0-1) that the CVE will be exploited in the next 30 days plus a percentile rank. License: FIRST.org free-for-any-use.
| Name | Required | Description | Default |
|---|---|---|---|
| cve_id | Yes | CVE identifier in CVE-YYYY-NNNNN form |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the return values (probability and percentile rank) and the data source. It does not cover error handling, rate limits, or data freshness, but it is adequate for a simple data retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is composed of three efficient sentences: purpose, output details, and license. Every sentence adds value, and the key behavioral information is front-loaded. No redundancy or unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one required parameter and no output schema, the description adequately covers the return values and source. It could be improved by mentioning error handling for invalid CVEs, but overall it is sufficiently complete for a straightforward tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage, describing the required CVE ID format. The description adds context about the source and output but does not provide additional parameter constraints or semantics beyond what the schema already offers. Thus, it meets the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets the EPSS probability for one CVE, specifying the source (FIRST.org) and the output (daily probability and percentile rank). This is distinct from sibling tools like get_cve_record or get_kev_catalog.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly indicates when to use this tool (to obtain exploitation likelihood scores) and mentions the license, which may inform usage restrictions. However, it does not explicitly contrast with alternatives or provide when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_hf_daily_papersAInspect
Get Hugging Face's editor-curated daily AI/ML papers feed with community upvotes and discussion counts. Each entry carries paperId, title (sanitized), summary, authors, publishedAt, submittedAt, upvotes, num_comments, thumbnail, hf_url, arxiv_url (when arxiv-style), github_repo, github_stars, ai_keywords. Different signal from get_arxiv_recent (firehose) and get_ai_trending_papers (citation-ranked). Refreshed daily at 14:15 UTC.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max papers to return (1-50). Default 20. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It lists returned fields, mentions title sanitization, arxiv_url conditional existence, refresh time, and content curation. It does not cover error cases or rate limits, but for a read-only tool this 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph with clear structure: purpose, returned fields, sibling comparison, refresh time. Every sentence provides unique value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description compensates by listing all returned fields and mentioning refresh schedule. It covers the essential return structure, though it omits pagination or ordering details. Given the tool's simplicity, this is nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single 'limit' parameter. The description restates the schema's default and range but adds no additional semantic meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool fetches Hugging Face's editor-curated daily AI/ML papers feed with community engagement metrics, and distinguishes it from sibling tools (get_arxiv_recent, get_ai_trending_papers) by signal type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It directly compares this tool to two siblings, explaining that it provides curated content with upvote/discussion data, unlike the firehose (get_arxiv_recent) or citation-ranked (get_ai_trending_papers). This helps an agent decide when 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.
get_kev_catalogAInspect
Get the CISA Known Exploited Vulnerabilities (KEV) catalog. Returns the most recent N entries plus catalog metadata. Refreshed daily at 06:30 UTC. License: US Government public domain. ~1500 actively-exploited CVEs.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Top-N most-recent entries (1-50) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses important behavioral traits: daily refresh at 06:30 UTC, public domain license, and approximate catalog size (~1500 CVEs). This adds value beyond the bare minimum.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four succinct sentences, front-loaded with purpose, then key details. No redundant or superfluous content. Every sentence contributes useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description mentions 'entries plus catalog metadata' giving a reasonable expectation. However, it could be more specific about metadata contents or whether pagination is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with the 'limit' parameter well-documented. The description repeats the 'most recent N entries' concept but does not add new semantic meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves the CISA KEV catalog, specifying it returns the most recent N entries and catalog metadata. This distinguishes it from sibling tools like get_cve_record or get_epss_score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining the KEV catalog but does not explicitly guide when to use this tool versus alternatives. No when-not-to-use or specific context is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_modelsAInspect
Get the model pricing and specs catalog across providers (Anthropic, OpenAI, Google, Meta, Mistral, Cohere, etc). Includes per-token pricing, context windows, capabilities, deprecation flags. Refreshed daily.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It correctly signals a read operation, states freshness ('Refreshed daily'), and lists included data fields. It does not mention potential side effects (none expected) or technical details like pagination, but given the simplicity of a parameterless catalog, this 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences with no superfluous words. Key information (purpose, scope, data fields, freshness) is front-loaded. Every sentence is essential.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the key aspects of the tool: what it does, what data it returns, and how fresh it is. Since there is no output schema, the description adequately explains the return content. Minor omissions like ordering or limits do not significantly impact completeness for a simple catalog.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, and the baseline for 0 parameters is 4. The description adds value by specifying what the tool returns (pricing, specs, providers, etc.), compensating for the absence of parameter documentation. It provides context beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a catalog of model pricing and specs across multiple providers, listing specific data points (per-token pricing, context windows, capabilities, deprecation flags) and provider names. It effectively distinguishes from sibling tools that target different data sources (e.g., CVE, EIA, SEC).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining model pricing and specs but does not explicitly state when to use or not, nor does it reference alternative tools. While the purpose is clear, the lack of explicit usage guidance (e.g., 'Use this for AI model info; for other data, use the respective get_* tools') limits the score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_news_articlesAInspect
Get the latest AI news articles aggregated from 12+ sources (Anthropic, OpenAI, Google, HuggingFace, TechCrunch, The Verge, Hacker News, etc). Polled every 10 min, deduplicated, sanitized for prompt injection. Returns up to 200 articles with title, snippet, source, and publishedAt.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max articles to return (1-200) | |
| category | No | Optional category filter (e.g. models, business, research) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses polling frequency (every 10 min), deduplication, sanitization, maximum articles (200), and returned fields. It does not mention rate limits or authentication, but the tool is read-only and low-risk, so minor gaps are acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences, front-loading the main verb and resource. Every sentence adds necessary information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of an output schema, the description explains the return format (title, snippet, source, publishedAt). The tool is simple, and all critical details (sources, freshness, limits, filtering) are covered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema coverage is 100% with both parameters described. The description adds value by providing example values for category ('models, business, research') and clarifying the limit range (1-200), which is not in the schema's default value description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and resource ('latest AI news articles'), clearly stating the tool's function. It distinguishes from sibling tools, which are all non-news related (e.g., CVE records, FDA events), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly states the tool's purpose, but does not provide explicit when-to-use or when-not-to-use guidance. However, given that no sibling tools serve a similar news-gathering function, the context is sufficient for correct selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_osv_advisory_by_idAInspect
Look up a single OSV.dev advisory by ID. Accepts GHSA / CVE / PYSEC / RUSTSEC / GO / OSV / DSA / ALPINE / DEBIAN / UBUNTU and other documented identifier prefixes. License: Apache 2.0.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Advisory ID, e.g. GHSA-r75f-5x8p-qvmc or CVE-2024-3094 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must compensate. It only mentions data license (Apache 2.0) but no traits like rate limits, auth needs, or side effects for this read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no waste. Front-loaded purpose, then accepted formats and license. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema or annotations, the description is minimal. It lacks details on return values or error behavior, leaving some gaps for an agent to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one parameter having a description. The description adds value by listing accepted identifier prefixes beyond the schema example, enhancing meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Look up a single OSV.dev advisory by ID' with specific verb and resource. It lists accepted identifier prefixes, distinguishing it from siblings like get_osv_advisory_for_package.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly indicates usage via identifier prefixes but does not explicitly state when to use this tool vs alternatives or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_osv_advisory_for_packageAInspect
Cross-ecosystem vulnerability advisory lookup via OSV.dev. Given an ecosystem (PyPI, npm, Go, crates.io, Maven, NuGet, RubyGems, Packagist, etc) and a package name (optional version), returns advisories affecting that package. License: Apache 2.0.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Package name | |
| version | No | Optional package version | |
| ecosystem | Yes | OSV ecosystem identifier (PyPI, npm, Go, crates.io, etc) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations were provided, so the description must convey behavioral traits. It notes the license (Apache 2.0) and that it returns advisories, but omits critical details like rate limits, authentication requirements, error handling, or whether it is 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loading the core purpose and then providing parameter context and license. Every sentence is necessary and no information is redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 3 parameters and no output schema. The description explains what it returns (advisories) but does not detail the return format, pagination, or error conditions. For a simple lookup, completeness is adequate but could be improved.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already has 100% coverage with descriptions for all parameters. The description adds value by listing example ecosystems and clarifying the 'ecosystem' field's role, but does not significantly enhance understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a cross-ecosystem vulnerability advisory lookup via OSV.dev, specifying the action (lookup), resource (advisories), and scope (by ecosystem and package). It distinguishes from siblings like get_osv_advisory_by_id.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lists valid ecosystems and mentions optional version, implying usage context. However, it does not explicitly state when to use this tool versus alternatives (e.g., get_osv_advisory_by_id) or provide any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_recent_earthquakesAInspect
Get recent earthquakes from the US Geological Survey's pre-built summary feeds. Choose a magnitude bucket (significant | 4.5 | 2.5 | 1.0 | all) and a time window (hour | day | week | month). Returns a flattened list with id, magnitude, place, time (ISO 8601), depth_km, longitude, latitude, tsunami flag, USGS detail URL. Upstream feeds refresh every minute. License: US Government public domain (17 USC §105).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max earthquakes to return (1-500). Default 50. | |
| period | No | Time window. One of: hour, day, week, month. Default day. | day |
| magnitude | No | Magnitude bucket. One of: significant, 4.5, 2.5, 1.0, all. Default 4.5. | 4.5 |
Tool Definition Quality
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 feed refresh rate (every minute) and license (public domain), which adds behavioral context beyond a simple read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: main action first, parameter options, return format, then additional details. Every sentence provides useful information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains return fields. It covers purpose, parameters, behavior, and license, making it fully complete for an 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters are fully described in the schema (100% coverage). The description adds value by listing enum values and return fields, providing context that helps an agent understand parameter choices.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves recent earthquakes from USGS feeds, with specific resource and action. It implicitly differentiates from sibling tools covering other domains, though no explicit distinction is made.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains how to use the tool by specifying magnitude bucket and time window options. It does not mention when not to use or alternatives, but the context is clear enough for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_sec_submissionsAInspect
Get entity metadata + recent filings for one company by CIK. Accepts canonical zero-padded CIK ("0000320193"), bare numeric ("320193"), or "CIK0000320193" prefixed. Returns full company profile (name, tickers, exchanges, EIN, SIC, addresses) plus the most-recent ~1000 filings. License: US Government public domain.
| Name | Required | Description | Default |
|---|---|---|---|
| cik | Yes | CIK in any zero-padding form, or CIK-prefixed |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the return content (company profile fields and approximate filing count) and the license. It implies read-only behavior by describing a 'get' operation, but does not explicitly state it is non-destructive or mention rate limits or authentication.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the purpose, then detailing accepted formats, return content, and license. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one required parameter and no output schema or nested objects, the description covers everything an agent needs: what it returns, the input format, and data licensing. It is fully complete for this simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the CIK parameter, but the description adds specific examples and clarifies the acceptable formats (zero-padded, bare, prefixed). This enriches the schema's static description significantly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get'), resource ('entity metadata + recent filings'), and scope ('for one company by CIK'). It distinguishes this tool from siblings like lookup_sec_company_ticker and search_sec_edgar, which focus on different aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly explains the three accepted CIK formats (zero-padded, bare numeric, CIK-prefixed), providing clear context for using the tool. However, it does not mention when not to use it or explicitly compare to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_status_summaryAInspect
Get the live operational status of every major AI service tracked by TensorFeed (Claude, ChatGPT, Gemini, Perplexity, Cohere, Mistral, HuggingFace, Replicate, Midjourney, etc). Polled every 2 min. Returns operational | degraded | down per service plus the most recent incident.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description discloses polling frequency (every 2 min), output format (operational/degraded/down plus incident). Adequately covers behavioral traits for a read-only tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no waste. Front-loaded with main action, followed by key details (polling, output). Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity (no params, no output schema), description fully covers purpose, update mechanism, and return format. Complete for intended use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 0 parameters, baseline is 4. Description adds no parameter info, but none needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb+resource: 'Get the live operational status of every major AI service tracked by TensorFeed', listing examples. Clearly distinguishes from siblings which are unrelated data retrieval tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context for when to use: when you need current operational status of major AI services. No explicit exclusion of alternatives, but siblings cover different domains so no confusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_weather_alertsAInspect
Get active US weather alerts from the National Weather Service. US-only coverage. Filter by 2-letter state code (area), exact event name (e.g. 'Tornado Warning', 'Heat Advisory'), severity (Extreme | Severe | Moderate | Minor | Unknown), urgency (Immediate | Expected | Future | Past | Unknown), and status (actual | exercise | system | test | draft). Returns a flattened alerts list with id, event, severity, urgency, headline, description, areaDesc, sent/effective/expires/ends, sender_name, web URL. Refreshed in real time upstream; cached 60s. License: US Government public domain (17 USC §105).
| Name | Required | Description | Default |
|---|---|---|---|
| area | No | 2-letter US state or territory code (CA, TX, PR, etc). Optional. | |
| event | No | Exact NWS event name (e.g. "Tornado Warning", "Heat Advisory"). Optional. | |
| limit | No | Max alerts to return (1-500). Default 50. | |
| status | No | One of: actual, exercise, system, test, draft. Optional. | |
| urgency | No | One of: Immediate, Expected, Future, Past, Unknown. Optional. | |
| severity | No | One of: Extreme, Severe, Moderate, Minor, Unknown. Optional. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It discloses real-time refreshing with 60s caching, license information, and the return structure (fields like id, event, severity, etc.). It does not mention rate limits or authentication, but for a public API this 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately long but well-organized, starting with purpose, then filters, then output, then cache/license. Each sentence adds value, though it could be slightly more compact by combining some filter details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description adequately explains the return format (flattened list with fields). It covers purpose, filters, cache behavior, and license. It does not cover edge cases like no results or errors, but for a simple read tool this is acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with descriptions for all 6 parameters. The description adds value by specifying enum values for severity, urgency, and status, and by listing the return fields, which compensates for the lack of an output schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves active US weather alerts from the National Weather Service, with a specific verb ('Get') and resource ('weather alerts'), and the geographic scope ('US-only') distinguishes it from unrelated siblings like 'get_news_articles' or 'get_recent_earthquakes'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists filtering options (state code, event, severity, urgency, status) and provides enum values, making it clear when to use the tool. It does not explicitly state when not to use it, but the distinct domain (weather) relative to siblings provides implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_sec_company_tickerAInspect
Resolve a stock ticker symbol (e.g. AAPL, MSFT, NVDA) or numeric CIK to the canonical SEC entity record. Returns ticker, CIK, company name, and exchange. Pair with get_sec_submissions to retrieve filings. License: US Government public domain.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker_or_cik | Yes | Ticker symbol (case-insensitive) or numeric CIK |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It implies a read-only lookup by describing resolution and returns, but does not explicitly state safety traits (e.g., idempotent, non-destructive). The license mention is a bonus but does not fully compensate for missing behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences plus a license note. The first sentence immediately states purpose and outputs, the second gives actionable pairing advice. Every word earns its place; no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter lookup with no output schema, the description provides sufficient context: what it does, what it returns, and how to extend with a sibling tool. It could explicitly state it is read-only, but the context is adequate for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already explains the parameter. The description adds examples (AAPL, MSFT, NVDA) and mentions case-insensitivity, which is slightly more than the schema but not semantically richer. Meets the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool resolves a ticker or CIK to a canonical SEC record and lists the returned fields (ticker, CIK, company name, exchange). It also distinguishes itself from sibling tools like search_sec_edgar and get_sec_submissions by specifying its specific resolution function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises to pair with get_sec_submissions to retrieve filings, providing clear usage context. While it does not state when not to use the tool, the pairing guidance is helpful for agents deciding between tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_fda_device_eventsAInspect
Query the FDA MAUDE medical device adverse event reports database. Returns device identifiers, problem codes, event narratives, patient outcomes. Useful for safety signal detection on FDA-cleared devices. License: openFDA CC0 1.0; commercial redistribution permitted.
| Name | Required | Description | Default |
|---|---|---|---|
| skip | No | Pagination offset (0-25000) | |
| sort | No | Sort by field | |
| limit | No | Max records to return (1-100) | |
| search | No | openFDA Lucene-style search expression. Examples: device.brand_name:medtronic, device.generic_name:pacemaker |
Tool Definition Quality
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 the license (CC0 1.0, commercial redistribution permitted), which is useful. However, it does not describe pagination limits, rate limits, authentication, or behavior on empty results. The read-only nature is implied but not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is three sentences with no wasted words. First sentence defines action and outputs, second gives use case, third provides license info. Front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description helps by listing returned fields, but does not explain pagination behavior, error handling, or date range defaults. The schema covers parameters well, but overall completeness is adequate for a simple query tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage, with each parameter described (e.g., search shows Lucene syntax examples). The description adds no extra parameter context beyond the schema, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it queries the FDA MAUDE database for medical device adverse event reports, specifies returned fields (device identifiers, problem codes, etc.), and notes the use case for safety signal detection. This differentiates it from sibling tools like query_fda_drug_events.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description mentions usefulness for safety signal detection and the license, but does not explicitly state when to use this tool versus alternatives like query_fda_drug_events. Usage context is implied by the database name and returned data types.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_fda_drug_eventsAInspect
Query the FDA FAERS adverse event reports database. Returns drug-event records with patient demographics, drug names, reaction terms (MedDRA-coded), outcomes, and seriousness flags. Uses openFDA Lucene-style search syntax (e.g. "patient.drug.medicinalproduct:aspirin"). License: openFDA CC0 1.0 Universal Dedication, FDA waiver of all copyright; commercial redistribution permitted.
| Name | Required | Description | Default |
|---|---|---|---|
| skip | No | Pagination offset (0-25000) | |
| sort | No | Sort by field (e.g. receivedate:desc) | |
| limit | No | Max records to return (1-100) | |
| search | No | openFDA Lucene-style search expression. Examples: patient.drug.medicinalproduct:aspirin, patient.reaction.reactionmeddrapt:headache+AND+receivedate:[20240101+TO+20251231] |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Although no annotations are provided, the description discloses the license (CC0), search syntax, and data fields returned (patient demographics, drug names, MedDRA-coded reactions, outcomes, seriousness). It does not cover rate limits or error handling, but the behavioral scope is adequately described.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences: the first states the verb and resource, the second lists returned fields, and the third gives search syntax examples and license. No redundancy, front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains return fields, but could elaborate on the JSON structure or potential error states. However, the examples and listed fields provide sufficient context for an 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds value by providing concrete examples of the 'search' parameter using openFDA Lucene syntax. For skip, limit, and sort, the schema already fully explains them, so the description's contribution is moderate but helpful.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies 'Query the FDA FAERS adverse event reports database' and lists returned data (demographics, drug names, reaction terms, outcomes, seriousness flags), distinguishing it from sibling tools like query_fda_device_events or query_fda_drug_labels.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus its siblings (e.g., device events, drug labels, recalls). The description implies usage for adverse event data but does not compare or contrast with other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_fda_drug_labelsAInspect
Query the FDA structured product labeling (SPL) database for prescription and OTC drugs. Returns indications, dosage, warnings, contraindications, adverse reactions, and pharmacology sections. License: openFDA CC0 1.0; commercial redistribution permitted.
| Name | Required | Description | Default |
|---|---|---|---|
| skip | No | Pagination offset (0-25000) | |
| sort | No | Sort by field | |
| limit | No | Max records to return (1-100) | |
| search | No | openFDA Lucene-style search expression. Examples: openfda.brand_name:tylenol, openfda.generic_name:metformin |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses license and openFDA attribution but does not mention rate limits, authentication, or any constraints beyond pagination from schema. Lacks explicit read-only or safety traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with clear front-loading of purpose. License sentence is useful but slightly extraneous; overall efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequately describes the types of information returned despite missing output schema. For a query tool with well-documented parameters, this provides enough context for an agent to decide when to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers 100% of parameters with descriptions (search, skip, limit, sort). Description adds no additional semantic value beyond what schema already provides, meeting baseline but not enriching.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it queries the FDA structured product labeling database for drugs, listing specific sections returned (indications, dosage, warnings, contraindications, adverse reactions, pharmacology). Distinct from sibling tools like query_fda_device_events or query_fda_drug_recalls by focusing on drug labels.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance. Does not mention alternative tools or prerequisites. Implies usage for retrieving label sections but lacks context for exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_fda_drug_recallsBInspect
Query the FDA drug enforcement (recall) database. Returns recall classification (Class I/II/III), reason, distribution, product description, and voluntary/mandatory flag. License: openFDA CC0 1.0; commercial redistribution permitted.
| Name | Required | Description | Default |
|---|---|---|---|
| skip | No | Pagination offset (0-25000) | |
| sort | No | Sort by field (e.g. report_date:desc) | |
| limit | No | Max records to return (1-100) | |
| search | No | openFDA Lucene-style search expression. Examples: classification:"Class+I", reason_for_recall:contamination |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It mentions the license (CC0) but does not disclose any behavioral aspects such as pagination limits beyond schema defaults, rate limits, or potential side effects. For a query tool, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences and front-loaded with the core action. It includes useful license info. While concise, it could drop the license line for brevity, but it's not overly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lists the return fields compensating for the lack of output schema. Input schema is fully covered. No missing context for a simple query tool, though it could clarify the classification system or prerequisites.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (all four parameters described). The description adds no extra meaning beyond the schema for parameters; it only lists return fields. Baseline 3 is appropriate since the schema already covers parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Query the FDA drug enforcement (recall) database' and lists specific fields returned (classification, reason, distribution, etc.). It distinguishes itself from siblings like query_fda_device_events and query_fda_food_recalls by focusing on drug recalls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus other FDA query tools (e.g., query_fda_drug_events). There are no when-not scenarios or alternative recommendations, leaving an AI agent without context to choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_fda_food_recallsAInspect
Query the FDA food enforcement (recall) database covering food products distributed in the US. Same shape as drug recalls. License: openFDA CC0 1.0; commercial redistribution permitted.
| Name | Required | Description | Default |
|---|---|---|---|
| skip | No | Pagination offset (0-25000) | |
| sort | No | Sort by field (e.g. report_date:desc) | |
| limit | No | Max records to return (1-100) | |
| search | No | openFDA Lucene-style search expression. Examples: reason_for_recall:listeria, classification:"Class+I" |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description implies a read-only query operation but does not detail rate limits, authentication, or other behavioral traits. The license info adds some context, but the description carries the full burden and could be more explicit about the tool's safety and idempotency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise—two sentences that convey the purpose, scope, shape comparison, and license. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 the essential context (what, where, shape, license) but lacks mention of return format or pagination behavior beyond schema params. It is fairly complete for a query tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does 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 tool description adds no further parameter details beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool queries the FDA food enforcement database for US food product recalls, and explicitly compares it to drug recalls ('same shape as drug recalls'), effectively differentiating it from the sibling tool query_fda_drug_recalls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context that this tool is specifically for food recalls in the US, and mentions the license allowing commercial redistribution. However, it does not explicitly state when to use this tool versus alternatives or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_sec_edgarBInspect
Lucene-style full-text search across the entire SEC EDGAR public-filings corpus since the 1990s. Forms include 10-K (annual), 10-Q (quarterly), 8-K (current event), DEF 14A (proxy), S-1 (IPO), 13F (institutional holdings), and ~70+ others. License: US Government public domain. The killer endpoint for finance and equity-research agents.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Search query (Lucene syntax supported) | |
| enddt | No | End date YYYY-MM-DD | |
| forms | No | Comma-separated form types (e.g. "10-K,10-Q,8-K") | |
| limit | No | Max hits (1-50) | |
| startdt | No | Start date YYYY-MM-DD |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry full burden. It fails to disclose behavioral traits such as authentication requirements, rate limits, pagination behavior, or result format. The only behavioral hint is the license, which is not operational.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences. The first two convey purpose and forms clearly. The license sentence is less relevant for agent invocation. The marketing sentence 'killer endpoint' is subjective and could be removed. Adequate but not tight.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and 5 parameters, the description covers purpose and form types fairly well. However, it lacks behavioral context (auth, limits) and does not mention result structure or error handling, leaving gaps for a agent to fully understand usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by listing example form types (10-K, 10-Q, etc.), but does not elaborate on other parameters like startdt/enddt beyond what schema provides. Some additional context but not extensive.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs Lucene-style full-text search across SEC EDGAR filings, specifying forms and use case. However, it does not explicitly differentiate from sibling tools like get_sec_submissions or lookup_sec_company_ticker, leaving some ambiguity for an agent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for finance and equity-research agents, but lacks explicit guidance on when to use vs. alternatives or when not to use. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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The server is experiencing an outage
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If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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