Clinicaltrials
Server Details
ClinicalTrials MCP — wraps ClinicalTrials.gov API v2 (free, no auth)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-clinicaltrials
- GitHub Stars
- 0
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Usage analytics
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Tool Definition Quality
Average 4.1/5 across 16 of 16 tools scored. Lowest: 2.9/5.
The clinical trial tools (ct_*) are distinct, but the presence of general-purpose tools like ask_pipeworx, entity_profile, and validate_claim creates overlap and confusion. An agent might struggle to choose between a specific ct_ tool and the generic ask_pipeworx for trial queries.
Naming is inconsistent: clinical trial tools use 'ct_' prefix, while others use varied styles like 'ask_pipeworx', 'compare_entities', and generic verbs like 'forget' and 'remember'. There is no uniform verb_noun pattern.
16 tools is a reasonable number, but the server is named 'Clinicaltrials' yet only 5 tools are clinical trial specific. The rest are general-purpose, making the scope mismatched and the count feel inflated for the intended domain.
Clinical trial coverage is decent (search, count, study details, updates, sponsor trials). Missing advanced features like trial results or adverse events, but core queries are supported. The unrelated tools do not fill gaps in the clinical trial domain.
Available Tools
16 toolsask_pipeworxBInspect
Answer a natural-language question by automatically picking the right data source. Use when a user asks "What is X?", "Look up Y", "Find Z", "Get the latest…", "How much…", and you don't want to figure out which Pipeworx pack/tool to call. Routes across SEC EDGAR, FRED, BLS, FDA, Census, ATTOM, USPTO, weather, news, crypto, stocks, and 300+ other sources. Pipeworx picks the right tool, fills arguments, returns the result. Examples: "What is the US trade deficit with China?", "Adverse events for ozempic", "Apple's latest 10-K", "Current unemployment rate".
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Your question or request in natural language |
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 mentions automatic tool selection and argument filling but does not disclose potential limitations, such as scope of data sources, response format, latency, or error behavior. The description lacks important behavioral 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?
Description is concise and front-loaded with the core purpose. Each sentence adds value, though the examples could be slightly more varied. No waste.
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's simplicity (one string param, no output schema), the description is mostly adequate but lacks detail on what happens after a question is asked (e.g., whether it returns a citation, confidence score, or raw text). Behavioral gaps reduce completeness.
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 baseline is 3. The description adds minimal extra meaning beyond the schema, mostly elaborating on the single parameter's purpose through examples.
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's purpose: answering natural language questions by automatically selecting the best data source and filling arguments. It distinguishes itself from sibling tools by acting as a general-purpose query interface rather than a specific data source tool.
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 when to use (when you have a plain English question) but does not explicitly state when not to use it or provide alternatives among sibling tools. Examples help, but no exclusion criteria are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_entitiesAInspect
Compare 2–5 companies (or drugs) side by side in one call. Use when a user says "compare X and Y", "X vs Y", "how do X, Y, Z stack up", "which is bigger", or wants tables/rankings of revenue / net income / cash / debt across companies — or adverse events / approvals / trials across drugs. type="company": pulls revenue, net income, cash, long-term debt from SEC EDGAR/XBRL for tickers like AAPL, MSFT, GOOGL. type="drug": pulls adverse-event report counts (FAERS), FDA approval counts, active trial counts. Returns paired data + pipeworx:// citation URIs. Replaces 8–15 sequential agent calls.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type: "company" or "drug". | |
| values | Yes | For company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description details returned data (e.g., revenue, adverse events) and URIs. Lacks error handling or data freshness but sufficient for 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?
Three sentences, front-loaded with purpose, no redundant words. Clearly 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?
No output schema, but description fully explains return values, resource URIs, and entity count range. Sufficient for agent invocation.
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 100%; description adds mapping from type to returned fields and format examples (tickers, drug names), significantly enhancing 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?
Clearly states the tool compares 2-5 entities side by side, with specific data for company and drug types. Differentiates from sequential calls, no sibling overlap.
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 replaces 8-15 sequential calls, indicating efficiency. Does not specify when not to use or list alternatives, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ct_count_by_conditionCInspect
Count trials for a condition (e.g., 'diabetes'). Returns breakdown by status and phase for landscape analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| phase | No | Optional phase filter: PHASE1, PHASE2, PHASE3, PHASE4 | |
| status | No | Optional status filter: RECRUITING, COMPLETED, etc. | |
| condition | Yes | Condition or disease (e.g., "breast cancer", "diabetes", "Alzheimer") |
Output Schema
| Name | Required | Description |
|---|---|---|
| condition | Yes | Condition queried |
| total_count | Yes | Total matching trial count |
| phase_filter | Yes | Applied phase filter or 'all' |
| status_filter | Yes | Applied status filter or 'all' |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description should disclose behavioral traits. It doesn't mention if the count is approximate or exact, if it includes all phases by default, or any rate limits. The description is minimal and lacks important 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?
The description is concise with two sentences. The first sentence clearly states the primary function. However, the second sentence about use cases could be integrated or made more specific. Overall, it's appropriately sized and front-loaded.
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 could explain what the output looks like (e.g., just a number? also grouped by phase?). The tool has 3 optional parameters, but the description doesn't guide on how filters affect counting. It's minimally complete but leaves gaps.
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 all parameters have descriptions. The description adds no additional parameter meaning beyond the schema. Baseline 3 is appropriate as the schema already documents parameters adequately.
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 states it counts clinical trials by condition, which is clear. However, it doesn't differentiate from sibling tools like ct_search, which also deals with clinical trials. The verb 'count' helps distinguish, but more explicit distinction would improve clarity.
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 mentions 'landscape analysis and competitive intelligence' as use cases, which is good. But it doesn't specify when not to use this tool or compare it to alternatives like ct_search or ct_get_study. No guidance on excluding other tools is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ct_get_studyAInspect
Get full trial details by NCT ID (e.g., 'NCT04567890'). Returns protocol, eligibility criteria, primary outcomes, sponsor, locations, and results.
| Name | Required | Description | Default |
|---|---|---|---|
| nct_id | Yes | ClinicalTrials.gov NCT identifier (e.g., "NCT05462717") |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 states returns complete protocol sections, which is helpful. However, it doesn't disclose whether the tool requires authentication, has rate limits, or what happens if the NCT ID doesn't exist (e.g., error behavior). The description adds moderate value beyond the schema but lacks depth.
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 that front-load the purpose and immediately provide scope. Every word adds value: 'full study details', 'by its NCT ID', and listing returned sections. No redundancy or filler.
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's simplicity (one parameter, no output schema, no annotations), the description is nearly complete. It covers what the tool does and what it returns. Minor gaps: no mention of error handling or output format, but for a straightforward lookup 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?
Schema coverage is 100%, so the description need not repeat parameter details. It adds context by explaining the tool's purpose (full study details) and the content returned (eligibility, outcomes, results), which clarifies what the parameter 'nct_id' is used for. This adds meaning beyond the schema's basic 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 clearly states the tool retrieves full study details for a clinical trial by NCT ID. It specifies the verb 'Get', the resource 'study details', and the identifier type 'NCT ID'. It also lists returned content (eligibility, outcomes, results), distinguishing it from siblings like ct_search or ct_count_by_condition.
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 when to use this tool: when needing full study details for a known NCT ID. It doesn't explicitly mention when not to use it or alternatives, but the context of sibling tools (e.g., ct_search for broader queries) provides implicit guidance. The clear purpose helps the agent decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ct_recent_updatesAInspect
Get recently posted or updated trials sorted by date. Returns NCT IDs, titles, status changes, and conditions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (1-100, default 20) | |
| query | No | Optional search term to narrow results |
Output Schema
| Name | Required | Description |
|---|---|---|
| studies | Yes | List of formatted trial summaries |
| total_count | Yes | Total recently updated trials |
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 clarifies sorting by last update date, but does not disclose other behaviors like rate limits, authentication needs, or whether results are cached.
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 with the action and result, then a one-sentence use case.
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 simple input schema with only two optional parameters and no output schema, the description adequately covers purpose and usage. It could mention return format or behavior when no results are found, but is complete enough for this complexity.
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 baseline is 3. The description adds value by indicating that the 'query' is 'Optional' and for narrowing results, but does not add meaning beyond the schema descriptions.
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 'recently updated or posted clinical trials' with clear sorting criteria. It distinguishes from siblings like ct_search by focusing on recency rather than general search.
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 states it is 'Good for monitoring pipeline changes,' providing a clear use case. However, it does not explicitly mention when not to use it or suggest alternatives among the listed siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ct_searchAInspect
Search clinical trials by keyword, condition, status (e.g., 'Recruiting'), or phase (e.g., 'Phase 2'). Returns NCT IDs, titles, status, enrollment, and sponsor info.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (1-100, default 10) | |
| phase | No | Filter by phase: EARLY_PHASE1, PHASE1, PHASE2, PHASE3, PHASE4 | |
| query | Yes | Search term (e.g., "GLP-1 receptor agonist", "breast cancer immunotherapy") | |
| status | No | Filter by overall status: RECRUITING, ACTIVE_NOT_RECRUITING, COMPLETED, TERMINATED, WITHDRAWN, ENROLLING_BY_INVITATION, SUSPENDED, NOT_YET_RECRUITING | |
| sponsor | No | Filter by sponsor name (e.g., "Pfizer", "Novo Nordisk") |
Output Schema
| Name | Required | Description |
|---|---|---|
| studies | Yes | List of formatted trial summaries |
| total_count | Yes | Total number of matching trials |
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 describes the search and filter behavior and the return structure, which is adequate but lacks details on pagination, rate limits, or data freshness. A neutral score is appropriate.
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, concise sentence that front-loads the key information: what it searches, by which filters, and what it returns. No 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 the complexity (5 parameters, no output schema), the description is minimal but covers the basic search and filter functionality. It does not explain the response structure beyond 'study count and array of matching trials', which may be insufficient for an agent to parse results.
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 description mentions keyword, status, phase, and sponsor as filter options, adding no semantics beyond the schema's parameter descriptions.
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 searches clinical trials by keyword and filters (status, phase, sponsor), and mentions the return value (count + array of trials). It distinguishes itself from siblings like ct_get_study (single study) and ct_count_by_condition (count only), though it could be more explicit about the difference.
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 searching trials with filters, but provides no explicit guidance on when to use this tool vs alternatives like ct_count_by_condition or ct_get_study. 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.
ct_sponsor_trialsAInspect
List all trials by sponsor or organization name. Returns status, phase, and conditions to map research pipelines.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (1-100, default 20) | |
| phase | No | Optional phase filter | |
| status | No | Optional status filter | |
| sponsor | Yes | Sponsor or company name (e.g., "Pfizer", "Novo Nordisk", "Moderna") |
Output Schema
| Name | Required | Description |
|---|---|---|
| sponsor | Yes | Sponsor name queried |
| studies | Yes | List of formatted trial summaries |
| total_count | Yes | Total trials by sponsor |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are empty, so the description carries full burden. It indicates read-only behavior (listing), but does not disclose rate limits, pagination, or data freshness. Lacks explicit statement that it is non-destructive.
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 concise two-sentence structure. The first sentence states purpose, the second adds context. 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?
The tool has 4 parameters (1 required) and no output schema. The description provides minimal additional context beyond the schema. It mentions pipeline analysis but does not explain return format, ordering, or error handling.
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 baseline is 3. The description does not add parameter semantics beyond the schema. It does not explain the relationship between parameters or provide examples of how filters interact.
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 lists clinical trials by sponsor, which is a specific verb+resource combination. It differentiates from siblings like ct_search (general search) and ct_count_by_condition (counts by condition).
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 says 'useful for pipeline analysis,' implying when to use, but does not explicitly state when not to use or mention alternatives like ct_search for broader queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discover_toolsAInspect
Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names + descriptions. Call this FIRST when you have many tools available and want to see the option set (not just one answer).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of tools to return (default 20, max 50) | |
| query | Yes | Natural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries") |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description states it returns the most relevant tools but does not specify the ranking algorithm, whether it uses semantic search, or any limitations (e.g., rate limits). No annotations provided, so some behavioral detail is missing.
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: first states purpose, second describes output, third gives usage guidance. No wasted words, front-loaded with key 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, description clarifies what is returned (names and descriptions). Tool has simple inputs; description covers essential aspects. Could mention what happens on empty results or errors, but not critical.
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% coverage with descriptions for both 'query' (natural language description) and 'limit' (max number). Description reinforces the natural language aspect, adding value beyond 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?
Description clearly states it searches a tool catalog and returns relevant tools with names and descriptions. The verb 'search' plus the resource 'Pipeworx tool catalog' makes the purpose specific and distinct.
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 says 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear when-to-use guidance and implies it's a discovery step before using other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
entity_profileAInspect
Get everything about a company in one call. Use when a user asks "tell me about X", "give me a profile of Acme", "what do you know about Apple", "research Microsoft", "brief me on Tesla", or you'd otherwise need to call 10+ pack tools across SEC EDGAR, SEC XBRL, USPTO, news, and GLEIF. Returns recent SEC filings, latest revenue/net income/cash position fundamentals, USPTO patents matched by assignee, recent news mentions, and the LEI (legal entity identifier) — all with pipeworx:// citation URIs. Pass a ticker like "AAPL" or zero-padded CIK like "0000320193".
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type. Only "company" supported today; person/place coming soon. | |
| value | Yes | Ticker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193"). Names not supported — use resolve_entity first if you only have a name. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses return format (pipeworx:// URIs) and performance characteristic (bundling multiple sources). Lacks explicit read-only indication, but the nature is implied.
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 wasted words. Each sentence adds distinct value: purpose, content, and exclusion guidance. Front-loaded with essential 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 low parameter count (2), full schema coverage, and no output schema, the description covers all necessary context: what data is returned, when to use alternatives, and how to prepare inputs.
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. Description adds extra guidance: 'Names not supported — use resolve_entity first,' which enhances schema description. No further parameter details 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?
The description clearly states 'Full profile of an entity' and lists specific data types included (SEC filings, revenue, patents, news, LEI). It distinguishes from siblings by mentioning 'For federal contracts call usa_recipient_profile directly' and implies replacement of 10-15 sequential calls.
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 states when to use (comprehensive profile) and when not to (federal contracts), and suggests using resolve_entity first if only a name is available. Provides clear alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetCInspect
Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key to delete |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states 'Delete' (destructive), but doesn't disclose whether deletion is permanent, if confirmation is needed, or if it affects other data. The description is too brief to cover behavioral 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?
Extremely concise (6 words) and front-loaded with the action. However, it is perhaps too terse, lacking context that would earn a 5.
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 simplicity (1 required param, no output schema), the description is minimal but complete enough to convey basic purpose. However, it lacks any behavioral or usage context that would help an agent decide to invoke 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?
Schema coverage is 100% with a single parameter 'key' described as 'Memory key to delete'. The description adds no additional semantic value 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?
The description clearly states the verb 'Delete', the resource 'stored memory', and the means 'by key'. It distinguishes from siblings like 'remember' (create) and 'recall' (retrieve), though it could explicitly contrast with them.
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 on when to use this vs alternatives. The tool is for deletion, but no context is given about prerequisites (e.g., memory must exist) or consequences (irreversible?). No mention of 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.
pipeworx_feedbackAInspect
Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | bug = something broke or returned wrong data. feature = a new tool or capability you wish existed. data_gap = data Pipeworx does not currently expose. praise = positive note. other = anything else. | |
| context | No | Optional structured context: which tool, pack, or vertical this relates to. | |
| message | Yes | Your feedback in plain text. Be specific (which tool, what error, what data was missing). 1-2 sentences typical, 2000 chars max. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description covers key behaviors: rate-limited to 5 per day per identifier and free. It does not detail what happens after submission (e.g., confirmation), but the provided information is sufficient for a feedback 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 two sentences plus a rate-limit note, all relevant and front-loaded. No superfluous content; every sentence adds necessary context.
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 is complete for the tool's purpose, covering all essential aspects: purpose, usage, content rules, and constraints. It could mention submission handling but is adequate given the tool's simplicity.
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 detailed parameter descriptions. The description adds value by specifying that the message should describe what was tried and exclude the end-user's prompt verbatim, enhancing the schema's guidance.
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 is for sending feedback to the Pipeworx team, listing specific categories (bug reports, feature requests, missing data, praise). It clearly distinguishes the tool from siblings that are data query or manipulation 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?
The description provides clear when-to-use guidance, specifying allowed feedback types and content guidelines. It mentions rate-limiting but does not explicitly contrast with sibling tools like ask_pipeworx, leaving some ambiguity about 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.
recallAInspect
Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.
| Name | Required | Description | Default |
|---|---|---|---|
| key | No | Memory key to retrieve (omit to list all keys) |
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 behavior: retrieving by key or listing all. It does not specify return format or error handling, but for a simple key-value retrieval, the description 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?
Two sentences, no fluff. Each sentence adds distinct information: retrieval method and usage context. Efficiently front-loaded.
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's simplicity (one optional parameter, no output schema, no nested objects), the description is complete enough. It covers the core functionality and usage hint. Minor gap: no mention of what happens if key doesn't exist.
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 describes the 'key' parameter with 100% coverage. The description adds context that omitting the key lists all memories, which is a key behavioral insight not in the schema. This adds value 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 verb 'Retrieve' and the resource 'memory by key', and distinguishes between retrieving a specific key and listing all memories. This effectively differentiates it from sibling tools like 'remember' (store) and 'forget' (delete).
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 says when to use it ('to retrieve context you saved earlier') and provides the alternative action (omit key to list all). It also implies when not to use it (e.g., for storing, use 'remember'). This gives clear guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recent_changesAInspect
What's new with a company in the last N days/months? Use when a user asks "what's happening with X?", "any updates on Y?", "what changed recently at Acme?", "brief me on what happened with Microsoft this quarter", "news on Apple this month", or you're monitoring for changes. Fans out to SEC EDGAR (recent filings), GDELT (news mentions in window), and USPTO (patents granted) in parallel. since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes + total_changes count + pipeworx:// citation URIs.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type. Only "company" supported today. | |
| since | Yes | Window start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring. | |
| value | Yes | Ticker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193"). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers behavioral traits: parallel fan-out to multiple sources for company type, return format (structured changes, count, URIs), and the safe read-only nature implied by 'list changes'. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences: purpose, detailed behavior (fan-out, date formats, return), and usage context. No filler, front-loaded with the core action.
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 outlines the return structure. It covers parameters, sources, and use cases. Minor gap: no details on the 'structured changes' schema, but sufficient for agent invocation.
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?
Adds meaning beyond schema by clarifying date formats (ISO/relative), providing examples, and explaining the value parameter can be ticker or CIK. Schema already covers basic descriptions, so the description enhances usability.
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 ('what's new') and resource ('entity'), specifies the entity type and data sources, and distinguishes from siblings like entity_profile and compare_entities by focusing on changes over time.
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 explicit usage examples ('brief me on what happened with X' and 'change-monitoring workflows'), guiding the agent on when to invoke. Does not explicitly list when not to use or name alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rememberAInspect
Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key (e.g., "subject_property", "target_ticker", "user_preference") | |
| value | Yes | Value to store (any text — findings, addresses, preferences, notes) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses persistence behavior (authenticated vs 24-hour anonymous) and implies non-destructive nature. No contradiction since annotations absent.
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: first explains function, second gives usage guidance. Every sentence adds value with 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?
Given simple tool with no output schema and only two parameters, description covers purpose, usage, and persistence behavior. Minor gap: does not mention if value is overwritten on same key, but schema hints at that.
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% and description does not add parameter details beyond the schema's descriptions. Baseline 3 is appropriate as schema does the work.
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 stores a key-value pair in session memory, specifying what it saves (intermediate findings, user preferences, context) and differentiates from siblings like 'recall' and 'forget'.
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 when to use it (save context across tool calls) and notes persistence differences (authenticated vs anonymous). However, it doesn't explicitly mention when not to use it or alternatives like 'recall' for retrieval.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resolve_entityAInspect
Look up the canonical/official identifier for a company or drug. Use when a user mentions a name and you need the CIK (for SEC), ticker (for stock data), RxCUI (for FDA), or LEI — the ID systems that other tools require as input. Examples: "Apple" → AAPL / CIK 0000320193, "Ozempic" → RxCUI 1991306 + ingredient + brand. Returns IDs plus pipeworx:// citation URIs. Use this BEFORE calling other tools that need official identifiers. Replaces 2–3 lookup calls.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type: "company" or "drug". | |
| value | Yes | For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry behavioral info. It describes the return format (ticker, CIK, company name, resource URIs) and states it's a read-like operation. It does not disclose auth requirements, rate limits, or error conditions, but adds some value over missing annotations.
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, each adding value. Purpose is front-loaded with clear action and scope. No redundant or irrelevant phrases. 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?
Given the tool has only two parameters and no output schema, the description covers the purpose, input formats, output contents, and benefit. It is self-contained for an agent to understand invocation and expectations.
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 has 100% coverage for both parameters. The description adds value by explaining the value parameter can be ticker, CIK, or name, with examples. It clarifies the enum for type (v1 supports company). This goes beyond the schema's basic descriptions.
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 entities to canonical IDs across Pipeworx data sources. It specifies the verb (resolve), resource (entity), and scope (single call). It distinguishes itself from sibling tools like ask_pipeworx and ct_* tools by focusing on entity resolution.
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 when to use the tool: for resolving a company entity using ticker, CIK, or name in a single call. It highlights benefit (replaces 2-3 lookup calls). It lacks explicit exclusions or alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_claimAInspect
Fact-check, verify, validate, or confirm/refute a natural-language factual claim or statement against authoritative sources. Use when an agent needs to check whether something a user said is true ("Is it true that…?", "Was X really…?", "Verify the claim that…", "Validate this statement…"). v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
| Name | Required | Description | Default |
|---|---|---|---|
| claim | Yes | Natural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year". |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It clearly describes input type, supported domains, and return values (verdict, structured form, actual value with citation, percent delta). However, it lacks details on potential limitations like case sensitivity or error handling.
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?
Single paragraph of ~60 words, highly information-dense with no redundancy. Every sentence adds value: scope, domain, output, and efficiency comparison.
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 one parameter, no output schema, and a specific domain, the description fully covers what an agent needs to know: domain, input format, output details, and use case distinction. No gaps.
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?
Only one parameter 'claim' with 100% schema description coverage, so baseline is 3. Description adds value by clarifying supported claim formats and the output it generates, enhancing 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?
Description clearly states the tool fact-checks natural-language claims against authoritative sources, specifically company-financial claims via SEC EDGAR+XBRL. It lists supported metrics and output types, distinguishing it from sibling tools that are not fact-checking focused.
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 states it supports company-financial claims for public US companies, indicating when to use. Also mentions it replaces 4-6 sequential agent calls, providing context for efficiency and suggesting when it might be preferable to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
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