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Server Details

Census MCP — U.S. Census Bureau housing-relevant APIs.

Status
Healthy
Last Tested
Transport
Streamable HTTP
URL
Repository
pipeworx-io/mcp-census
GitHub Stars
0

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Tool DescriptionsA

Average 4/5 across 12 of 12 tools scored. Lowest: 3.3/5.

Server CoherenceC
Disambiguation3/5

The ask_pipeworx tool is a meta-tool that can answer questions across data sources, potentially overlapping with specific census tools. However, the descriptions for census tools are clear, and memory tools are distinct. Some ambiguity remains.

Naming Consistency2/5

Tool names are inconsistent: some use 'census_' prefix (census_acs, census_building_permits), while others are generic verbs (ask_pipeworx, forget, recall, remember, compare_entities, resolve_entity). No uniform pattern.

Tool Count4/5

With 12 tools, the count is within a reasonable range for a server. However, the scope seems broader than just Census data, including memory and entity tools, which makes the set slightly expansive.

Completeness2/5

For a server named 'Census', it covers ACS, building permits, homeownership, and housing starts but misses key datasets like population, income, and employment. The inclusion of unrelated tools (memory, entity resolution) does not fill these gaps.

Available Tools

16 tools
ask_pipeworxA
Read-only
Inspect

PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 1,423+ tools across 392+ verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1".

ParametersJSON Schema
NameRequiredDescriptionDefault
questionYesYour question or request in natural language
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses the tool auto-selects sources and fills arguments, but lacks details on data sources or limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise and front-loaded with purpose, followed by examples. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given low complexity (1 param, no nested objects, no output schema), description is adequate but could mention scope of data sources or error handling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds natural language usage context but does not exceed what the schema provides for the single parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool accepts plain English questions and returns answers by selecting the best data source, with concrete examples distinguishing it from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage via 'just describe what you need' but does not specify when not to use it or alternatives among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

census_acsA
Read-only
Inspect

Search American Community Survey data by geography and variable code (e.g., B25077_001E for median home value). Returns housing ownership rates, rental costs, vacancy rates, and home values.

ParametersJSON Schema
NameRequiredDescriptionDefault
yearNoSurvey year (default 2022). ACS 5-year estimates available from 2009 onward.
_apiKeyYesCensus API key
geographyYesGeographic level and filter using Census "for" syntax (e.g., "state:06" for California, "county:*" for all counties, "state:*" for all states, "county:037&in=state:06" for LA County).
variablesYesComma-separated variable codes to retrieve (e.g., "NAME,B25001_001E,B25077_001E"). Always include NAME for place names.

Output Schema

ParametersJSON Schema
NameRequiredDescription
yearYesSurvey year queried
resultsYesArray of objects with Census data
geographyYesGeographic filter used
variablesYesVariable codes requested
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are empty, so the description carries the full burden. It explains the data source and common variables but does not disclose behavioral traits like rate limits, authentication requirements beyond the API key, or whether the tool is read-only. It adds context beyond schema but lacks detail on API behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with a clear purpose and helpful examples. It is front-loaded with the main function and then provides variable codes. Could be slightly more structured with explicit parameter details, but overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 4 parameters, no output schema, and no annotations, the description covers the core purpose and key variable codes. It lacks information on return format or pagination, but for a simple data retrieval tool, it is largely complete. Sibling differentiation is implicit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds context about variable codes and geography examples, which is helpful but doesn't go beyond what a well-documented schema provides. No new meaning for parameters like _apiKey beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets ACS 5-year data from the U.S. Census Bureau, specifies it's for housing statistics, and lists common variable codes. It differentiates from sibling tools like census_building_permits or census_homeownership by focusing on core housing data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for housing-related ACS data but does not explicitly say when to use this over other census tools. It provides helpful variable codes but lacks direct comparison to sibling tools for exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

census_available_datasetsA
Read-only
Inspect

Discover Census datasets and their variables. Returns dataset names, descriptions, and variable codes (e.g., B25001_001E) for querying with other census tools.

ParametersJSON Schema
NameRequiredDescriptionDefault
_apiKeyNoCensus API key (not required for this endpoint but accepted for consistency)

Output Schema

ParametersJSON Schema
NameRequiredDescription
datasetsYesHousing-related Census datasets
total_housing_relatedYesCount of housing-related datasets returned
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It clarifies that no API key is needed (a key behavioral trait), and that the endpoint is for discovery, not data retrieval. It doesn't disclose response format or limits, but given the simplicity, 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, no wasted words. Each sentence serves a distinct purpose: action, requirement, and utility. Information is front-loaded with the verb.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero required parameters, no output schema, and a simple list operation, the description covers the essential purpose and behavioral aspects. It could mention that the output is a list of dataset objects, but this is not critical.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description does not add any parameter-specific meaning beyond what the schema provides. The _apiKey parameter is already well-documented in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses specific verbs ('List') and a clear resource ('Census Bureau datasets'). It explains the tool's utility for discovering dataset identifiers, descriptions, and variables, distinguishing it from data-querying siblings like census_acs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states 'No API key required' and positions the tool as a prerequisite step before querying specific data. However, it does not mention when not to use it or list alternative tools explicitly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

census_building_permitsB
Read-only
Inspect

Check monthly building permits for new residential construction by geography. Returns count of authorized privately-owned housing units and construction activity trends.

ParametersJSON Schema
NameRequiredDescriptionDefault
timeYesTime period (e.g., "2024-01" for January 2024, "from+2023-01+to+2024-01" for a range).
_apiKeyYesCensus API key
variablesYesComma-separated variables (e.g., "PERMIT" for total permits, "PERMIT_1UNIT" for single-family). Use census_available_datasets to discover variables.
category_codeNoCategory filter (e.g., "TOTAL" for total, "1UNIT" for single-family). Optional.

Output Schema

ParametersJSON Schema
NameRequiredDescription
timeYesTime period queried
resultsYesArray of monthly building permit records
variablesYesVariable codes requested
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are empty, so the description must cover behavioral traits. It correctly indicates it is a data retrieval tool (not destructive), but does not disclose any rate limits, data freshness, or potential errors. However, it does clarify it tracks 'new privately-owned housing units authorized by building permits,' which sets expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences that clearly state purpose and source. No redundant or filler content. Front-loaded with key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 4 parameters (100% schema coverage) and no output schema, the description adequately states what data is returned (monthly building permits data) but does not mention response format or limitations. It is sufficient for basic understanding but could be more complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so each parameter is already described in the schema. The description adds no extra meaning beyond what the schema provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it gets monthly building permits data from the Census Bureau residential construction survey. The description specifies the resource (Census Bureau building permits) and the scope (tracks new privately-owned housing units authorized by building permits), distinguishing it from siblings like census_acs or census_homeownership.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like census_housing_starts or census_available_datasets. Does not mention prerequisites or 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.

census_homeownershipA
Read-only
Inspect

Check quarterly homeownership rates by geography. Returns percentage of owner-occupied housing units from the Housing Vacancy Survey.

ParametersJSON Schema
NameRequiredDescriptionDefault
timeYesTime period in YYYY-QN format (e.g., "2024-Q1" for Q1 2024). Use "from+2020-Q1+to+2024-Q1" for a range.
_apiKeyYesCensus API key

Output Schema

ParametersJSON Schema
NameRequiredDescription
timeYesTime period queried
metricYesMetric type
resultsYesArray of homeownership rate records
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are empty, so the description must carry behavioral transparency. It does not disclose any side effects, authorization requirements, or return format details. However, the tool appears to be a read-only data query, which is implied but not stated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences that front-load the purpose and metric. No redundant information; every word adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has only 2 parameters with 100% schema coverage and no output schema or nested objects. The description is minimally complete: it explains what the tool does and the metric, but lacks details like response format or error conditions, which would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the description does not need to add param details. It adds no extra meaning beyond the schema, which is sufficient for a baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns quarterly homeownership rates from a specific survey, with the exact metric defined as the percentage of owner-occupied units. This is a specific verb+resource combination that distinguishes it from sibling tools like census_acs or census_building_permits.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for accessing homeownership data from HVS, but does not explicitly state when to use it over other census tools or provide alternatives. No exclusions or context about data availability are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

census_housing_startsB
Read-only
Inspect

Get residential construction pipeline by geography: new starts, units under construction, and completed units. Returns housing supply and activity trends.

ParametersJSON Schema
NameRequiredDescriptionDefault
timeYesTime period (e.g., "2024-01" for January 2024).
regionNoCensus region filter (e.g., "NE" for Northeast, "MW" for Midwest, "S" for South, "W" for West). Optional.
_apiKeyYesCensus API key
variablesYesComma-separated variables (e.g., "STARTS" for housing starts, "UNDER_CONSTRUCTION", "COMPLETIONS").

Output Schema

ParametersJSON Schema
NameRequiredDescription
timeYesTime period queried
regionYesCensus region or 'all' if none specified
resultsYesArray of housing starts records
variablesYesVariable codes requested
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are empty, so the description must cover behavioral traits. It mentions it gets data from the Census Bureau but does not disclose any API rate limits, required authentication beyond the API key, or potential response size. The description is neutral and not misleading.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is concise and front-loaded with the action. It includes key data types but could be slightly more structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that the tool has 4 parameters, no output schema, and no annotations, the description is adequate but could mention output format or pagination. It is minimally viable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters. The description does not add new meaning beyond listing the data types. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it gets residential construction data from the Census Bureau, specifying three data types: housing starts, units under construction, and completions. It distinguishes itself from sibling tools like census_building_permits and census_homeownership by focusing on housing starts and construction activity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving housing construction data but does not explicitly state when to use this tool versus alternatives like census_building_permits. It provides no guidance on prerequisites or data frequency.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

compare_entitiesA
Read-only
Inspect

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).
Behavior4/5

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 returned data fields for each type, mentions paired data and resource URIs, and implies a read-only operation. It lacks explicit safety or authorization details but is fairly comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise (under 60 words) with no redundant information. Every sentence adds value, clearly communicating the tool's purpose, types, data, and efficiency benefit.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description adequately explains the return structure and content. It covers both entity types, specifies min/max entities, and mentions resource URIs. The agent can fully understand what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the description adds significant meaning beyond the schema by detailing the specific metrics returned for each entity type and clarifying input formats (tickers/CIKs vs. drug names).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Compare', specifies the resource 'entities', and clearly differentiates between 'company' and 'drug' types with distinct metrics. No sibling tool performs comparison, so it stands out.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states it replaces 8–15 sequential agent calls, implying efficiency, but does not explicitly state when to use it versus alternatives. However, since no sibling tool does comparison, the usage context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

discover_toolsA
Read-only
Inspect

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).

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries")
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must cover behavioral traits. It discloses that it searches a catalog and returns relevant tools with names and descriptions, which is a read-only, non-destructive operation. The description implies it is a search tool, but does not mention any side effects or access restrictions. The lack of annotations raises the burden, but the description adequately conveys the search behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, each earning its place: first states the core action, second states the output, third provides strategic usage advice. It is front-loaded and no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 params, no output schema, no nested objects) and the context of many sibling tools, the description is complete. It explains what it does, when to use it, and what it returns. No missing information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining that the query is a 'Natural language description of what you want to do' with examples, and for limit it provides default and max values. This enhances understanding beyond the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Search' and the resource 'Pipeworx tool catalog', and specifies the purpose 'by describing what you need. Returns the most relevant tools with names and descriptions.' This distinguishes it from sibling tools, which are specific data tools, as this is a meta-tool for discovering other tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear guidance on when to use it (as a first step) and the context (many tools available), and implies it is an alternative to manually searching through sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

entity_profileA
Read-only
Inspect

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".

ParametersJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today; person/place coming soon.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193"). Names not supported — use resolve_entity first if you only have a name.
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must disclose behavioral traits. It mentions that the tool returns pipeworx:// citation URIs and replaces 10-15 sequential calls, which is useful but does not explicitly state whether the operation is read-only or has side effects. The description adds some behavioral context but is not fully transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise, with two sentences and a brief list. It front-loads the core purpose and immediately provides actionable details. Every sentence earns its place, with no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description provides a good overview of what the tool returns (data sources and URI format). It also explains its efficiency benefit. However, it lacks a detailed output structure description, which would improve completeness given the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description does not add significant new meaning beyond the schema; it lists data sources but that pertains to output, not parameters. The parameter descriptions in the schema already explain type and value in detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves a full profile of an entity, listing specific data sources (SEC filings, XBRL, patents, news, LEI). It distinguishes itself from siblings by mentioning an alternative tool (usa_recipient_profile) and implies use cases. The verb 'Full profile' and resource entity are specific and actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use this tool (for company profiles) and when not (for federal contracts, call usa_recipient_profile instead). The input schema's parameter description also advises using resolve_entity if only a name is available, providing clear guidance on prerequisites and alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

forgetB
Destructive
Inspect

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key to delete
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavioral traits. It states deletion but does not mention whether deletion is permanent, requires confirmation, or affects related memories. This is insufficient for a destructive operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, efficient and front-loaded. However, it could benefit from a brief behavioral note (e.g., permanence) without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is too brief. For a single-parameter deletion tool, it should clarify irreversibility, scope (e.g., current user only), and any confirmation needed. The current description leaves ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description does not add meaning beyond the schema's 'key' parameter description. The parameter description in the schema is adequate, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Delete a stored memory by key' uses a specific verb ('Delete') and resource ('stored memory by key'), clearly distinguishing it from siblings like 'remember' (store) and 'recall' (retrieve).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool vs alternatives, but its name and purpose imply deletion. The sibling tools 'recall' and 'remember' suggest complementary operations, but no guidance is provided on prerequisites or scenarios.

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
typeYesbug = 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.
contextNoOptional structured context: which tool, pack, or vertical this relates to.
messageYesYour feedback in plain text. Be specific (which tool, what error, what data was missing). 1-2 sentences typical, 2000 chars max.
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses rate limiting (5 per day per identifier) and indicates the tool is free, providing basic behavioral context. However, without annotations, it does not elaborate on data handling, privacy, or confirmation behavior, which is acceptable for a simple 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is 4 sentences, all essential: action, use cases, message guideline, and rate limit. No redundancy or fluff, highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the feedback tool's simplicity (3 params, no output schema), the description covers purpose, message formatting, and constraints. Missing confirmation or response details, but not critical for this tool type.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by prompting users to describe their Pipeworx actions in the message and enumerating the purpose of each 'type' enum value, going slightly beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Send feedback to the Pipeworx team' (clear verb+resource) and lists specific use cases (bug reports, feature requests, etc.), distinguishing it from all sibling tools which focus on data retrieval or memory.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear guidance on when to use (feedback types) and how to write messages (describe Pipeworx context, no user prompt verbatim), and mentions rate limits. It lacks explicit 'do not use' conditions, but the positive instructions are strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

recallA
Read-only
Inspect

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
keyNoMemory key to retrieve (omit to list all keys)
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, description must disclose behavior. It states retrieval and listing, but doesn't mention any side effects or limitations (e.g., memory persistence, deletion behavior).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, clear and front-loaded. First sentence states purpose and usage, second sentence provides usage context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given simple tool with one optional parameter and no output schema, description covers key usage. Could add info about return format or memory lifecycle, but adequate for current complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already covers parameter well (100% coverage). Description adds context that omitting key lists all, which schema implies but description makes explicit.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states verb 'retrieve' and resource 'memory', specifies two modes (by key or list all) and distinguishes from 'remember' and 'forget' siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use (retrieve context saved earlier) and how to list all (omit key). Could mention when not to use (e.g., for new information, use remember).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

recent_changesA
Read-only
Inspect

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so description carries full burden. It discloses parallel fan-out to three sources, accepted date formats, and return structure (changes, count, URIs). Does not mention potential delays or error handling, but is good for a 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (4 sentences), front-loaded with purpose, and logically structured from general behavior to parameter details and use cases. No superfluous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description adequately explains return structure. Covers fan-out behavior and parameter semantics. Missing pagination or limits on results, but overall sufficient for a change-monitoring tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds value by explaining 'since' formats (ISO vs relative) with examples, and clarifies that only 'company' type is supported, going beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves changes for an entity since a given time, specifying behavior for company type (SEC, GDELT, USPTO). It distinguishes from siblings like 'entity_profile' by focusing on recent changes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly recommends using for 'brief me on what happened with X' or change-monitoring workflows. Lacks explicit when-not-to-use or direct comparison to alternatives, but guidance is clear and actionable.

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses that authenticated users get persistent memory and anonymous sessions last 24 hours, which is important behavioral context. It does not mention any destructive effects or rate limits, but these are not likely needed for a memory store.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, each adding essential information: purpose, typical use cases, and persistence behavior. No wasted words; information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and simple input schema, the description is complete enough. It explains what the tool does, when to use it, and a key behavioral detail (persistence). It could mention return value or error cases, but not strictly necessary for this simple tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds minimal parameter information (key and value) but does not elaborate beyond what the schema provides. Examples are given in schema descriptions, not in the tool description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool stores a key-value pair in session memory, with a specific verb ('store') and resource ('session memory'). It distinguishes from siblings like 'recall' (retrieve) and 'forget' (delete) by its purpose of saving data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says to use this tool for saving intermediate findings, user preferences, or context across tool calls. It provides context but does not explicitly state when not to use it or mention alternatives, though siblings are implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

resolve_entityA
Read-only
Inspect

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valueYesFor company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin").
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the tool's behavior: single call, v1 supports only company, accepts three identifier formats, and returns canonical IDs and URIs. It does not explicitly state read-only or no side effects, but for a lookup 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise—two sentences with no waste. It front-loads the purpose and efficiently conveys key details (v1 restriction, accepted inputs, outputs). Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description details the return values (ticker, CIK, company name, URIs). For a simple lookup tool with two parameters, the description is complete and leaves no major gaps in understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The description adds value by providing concrete examples (e.g., 'AAPL', '0000320193', 'Apple') that clarify the value parameter's accepted formats, going beyond the schema's generic description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool's purpose: 'Resolve an entity to canonical IDs across Pipeworx data sources in a single call.' It specifies the supported entity type (company) and input formats (ticker, CIK, name), and highlights it replaces multiple lookup calls, clearly distinguishing it from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context by specifying the input formats and noting it replaces 2–3 lookup calls, implying efficiency. However, it does not explicitly state when not to use this tool or mention alternatives, though sibling tools are unrelated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

validate_claimA
Read-only
Inspect

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).

ParametersJSON Schema
NameRequiredDescriptionDefault
claimYesNatural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description must convey behavior. It discloses return values (verdict, structured form, actual value with citation, percent delta) and notes the external data source. However, it omits details like latency, rate limits, or error conditions for a tool making external API calls.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each with distinct value: purpose, domain/source, and return+efficiency. No filler or redundancy. Information is front-loaded and scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With one parameter and no output schema, the description covers the essential context: what it does, what claims it handles, what returns to expect, and why it's efficient. It lacks an explanation of verdict meanings or examples of output, but these are not critical for a well-documented schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single parameter 'claim', so baseline is 3. The description adds example values and clarifies it's a natural-language claim, but the schema already provides a clear description. Minimal additional meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the tool's purpose: fact-checking natural-language claims, with a specific domain (US public company financials). It provides examples ('Apple's FY2024 revenue...'), distinguishes from sibling tools like census_acs or entity_profile, and uses a specific verb+resource combination.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states the supported claim types (company-financial, revenue/net income/cash for public US companies) and mentions the data source (SEC EDGAR + XBRL). It implies usage context by noting it replaces 4-6 sequential agent calls, but does not directly enumerate when to avoid the tool or compare 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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