Noaa
Server Details
NOAA Weather MCP — National Weather Service forecasts and alerts
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-noaa
- GitHub Stars
- 0
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Usage analytics
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Tool Definition Quality
Average 4/5 across 12 of 12 tools scored. Lowest: 3.1/5.
Tools like ask_pipeworx, compare_entities, and entity_profile have overlapping purposes; ask_pipeworx is a meta-tool that can perform tasks of other tools, causing potential confusion about when to use specialized tools vs. the general one.
Most names follow a verb_noun or noun_noun pattern with underscores (e.g., get_forecast, compare_entities), but a few are bare verbs (forget, recall, remember) or have non-standard order (entity_profile), so it's not perfectly consistent.
With 12 tools covering weather, entity lookup, memory, and meta-queries, the count is well-scoped for a general-purpose data access server; each tool has a clear role and none feel redundant.
The toolset covers querying well (weather, companies, drugs, memory) but lacks tools for modifying or creating entities beyond memory (no create/update/delete for data sources), relying on ask_pipeworx to fill gaps, which creates some dead ends.
Available Tools
14 toolsask_pipeworxAInspect
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?
Description explains that the tool internally 'picks the right tool, fills the arguments, and returns the result', revealing its orchestration behavior. With no annotations provided, this transparency about internal logic is valuable and sets correct expectations.
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 at about 60 words, with key information front-loaded. The first sentence immediately states the tool's purpose, followed by how it works and examples. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple question-answering tool with one parameter, the description is complete. It explains the tool's behavior, gives examples, and sets expectations. No output schema exists, but the description implies a natural language answer, which is sufficient.
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 only one parameter 'question' with 100% description coverage ('Your question or request in natural language'). The description adds context by showing example questions, which is helpful but not strictly required since the schema already covers the parameter's purpose.
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 accepts plain English questions and returns answers by selecting the best data source. Provides concrete examples like 'What is the US trade deficit with China?' which clarifies scope and distinguishes 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description explicitly says to 'just describe what you need' and gives examples, providing clear usage guidance. Does not explicitly mention when NOT to use it or suggest alternatives, but the context of sibling tools like 'discover_tools' and 'get_stations' implies this is the general-purpose Q&A tool.
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?
Describes return data (paired data + resource URIs). Discloses no destructive actions. With no annotations, description adequately covers 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?
Four sentences, each with unique information. No redundancy. 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?
Covers purpose, parameters, return format, efficiency benefit. For a tool with no output schema, description provides sufficient context.
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 already covers parameters (type with enum, values with min/max). Description adds specific fields compared for each type, 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?
Clearly states verb 'compare' and resource 'entities'. Distinguishes from sibling tools by specifying it does side-by-side comparison, not single entity lookup.
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 scenarios: company vs drug. Mentions efficiency gain (replaces 8-15 calls). Lacks 'when not to use', but sufficient for this tool.
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?
Without annotations, the description explains that it returns the most relevant tools with names and descriptions, which is sufficient for an agent to understand behavior. It doesn't mention edge cases like empty results or performance, but is clear enough.
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, each adding value: purpose, return type, and usage context. No wasted words, though slightly more verbose than strictly necessary.
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 (2 params, no output schema, no nested objects), the description covers its role and usage adequately. Could mention if results are ordered by relevance, but not essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description does not add extra semantics beyond the schema's parameter descriptions, but the schema already provides adequate detail.
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: searching the Pipeworx tool catalog by describing needs, and it distinguishes itself from siblings by instructing to call it FIRST when many tools are available.
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 guidance: 'Call this FIRST when you have 500+ tools available' indicates when to use it, and suggests it helps find the right tools, distinguishing from sibling tools like ask_pipeworx.
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, but the description discloses it returns citations in pipeworx:// format and bundles many sources. Implies it is a read operation without destructive side effects. Could explicitly state it is read-only or mention rate limits, but overall behavior is well explained.
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 comprehensive and each sentence serves a purpose. It is front-loaded with the main function. Could be more structured (e.g., bullet points), but remains efficient for the information density.
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 two simple parameters and no output schema, the description is quite complete: it explains input types with examples, notes what is not included (federal contracts), and mentions the URI format for citations. Missing output structure details but overall sufficient.
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 descriptive text for both parameters. The description adds value by clarifying that type only supports 'company' currently and that value accepts ticker or CIK, with names requiring prior resolution via resolve_entity. This goes 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?
Clearly states it returns a full entity profile across multiple Pipeworx packs, listing specific data sources (SEC filings, revenue, patents, news, LEI). Distinguishes from sibling tools by noting it replaces 10-15 sequential calls and explicitly points to usa_recipient_profile for federal contracts.
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 tells when to use this tool (for company profiles across packs) and when not (federal contracts: use usa_recipient_profile). Also advises to use resolve_entity if only a name is available, providing clear decision context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetBInspect
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 are provided, so the description carries the full burden. It states the action 'delete' but does not disclose whether deletion is permanent, requires confirmation, or any side effects. The description is minimal.
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 sentence with no wasted words, perfectly concise for a simple delete operation.
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 (1 required param, no output schema), the description is adequate but lacks behavioral context such as permanence of deletion or error handling. For a simple tool, it's minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond what the schema provides for the single 'key' parameter.
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 'Delete a stored memory by key' clearly states the verb 'Delete' and the resource 'stored memory', and specifies the parameter 'key'. It distinguishes itself from siblings like 'recall' (retrieve) and 'remember' (store).
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 gives no guidance on when to use this tool vs alternatives like 'forget' vs 'remember' or 'recall'. There is no mention of prerequisites or contexts where deletion is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_alertsAInspect
Check active weather alerts for a US state (e.g., "CA", "NY", "TX"). Returns alert type, severity, affected areas, and descriptions.
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Two-letter US state code (e.g. "CA", "NY") |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. Describes scope (currently active, US state) but doesn't disclose rate limits, data freshness, or return format. Adequate but minimal.
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, clear sentence front-loads key information (verb, resource, scope) with examples. 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?
Simple tool with 1 parameter and no output schema. Description covers purpose and parameter usage. Missing details like data source, update frequency, or response structure, but given simplicity, it's adequate.
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 reinforces the parameter format ('e.g. CA, NY, TX') and purpose. No additional meaning beyond schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb 'Get', resource 'weather alerts', and scope 'currently active for a US state'. Distinguishes from sibling tools like get_forecast and get_stations.
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?
Implies usage context (US states, active alerts) but provides no explicit when-to-use or alternatives. Could benefit from noting that this is for current alerts only, not historical.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_forecastAInspect
Get a multi-day NOAA NWS weather forecast for a US location. US ONLY (50 states + territories) — for non-US coordinates use the "weather" pack instead. Returns temperature, precipitation, wind, and conditions per period.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | Latitude of the location (e.g. 37.7749) | |
| lon | Yes | Longitude of the location (e.g. -122.4194) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the data source (National Weather Service) and that it returns a multi-day forecast. However, it does not mention rate limits, data freshness, or whether the forecast is text-based or data-structured. With no annotations, a score of 3 is adequate but has gaps.
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 sentence, concise and front-loaded. It contains no unnecessary words and clearly conveys the tool's purpose. It earns its place without 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 low complexity (2 simple parameters, no output schema, no nested objects), the description is reasonably complete. It explains the tool's purpose and data source. It could mention that it returns a forecast (but that is implied by 'forecast'). No output schema exists, so return format is not documented, but for a forecast tool, a 4 is acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% coverage with descriptions for both parameters (lat and lon). The description adds no extra meaning beyond what the schema provides (e.g., doesn't mention valid ranges for lat/lon). Baseline 3 is appropriate since schema already documents the parameters well.
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 'Get a multi-day weather forecast' for a latitude/longitude location using the National Weather Service. It specifies the verb 'get', the resource 'forecast', and the data source. However, it does not differentiate from siblings like 'get_alerts' or 'get_stations', but the sibling names are distinct enough.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining a forecast given lat/lon, which is clear. However, it provides no guidance on when to use this tool vs alternatives (e.g., 'get_alerts') or any prerequisites like API key requirements. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_stationsAInspect
Find weather observation stations in a US state (e.g., "CA", "NY", "TX"). Returns station IDs, names, locations, and observation types.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of stations to return (default: 20) | |
| state | Yes | Two-letter US state code (e.g. "CA", "NY") |
Output Schema
| Name | Required | Description |
|---|---|---|
| count | Yes | Number of weather stations returned |
| state | Yes | Two-letter US state code |
| stations | Yes |
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 correctly identifies a read operation but does not disclose any additional behavioral traits such as pagination, rate limits, or whether it returns current vs historical data.
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 sentence, zero waste, front-loaded with the action and resource. Perfectly concise.
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 2 parameters (1 required), no output schema, and no annotations. The description is minimal but sufficient for a straightforward listing tool. However, it could benefit from mentioning that stations are for weather observation or that state is required.
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 additional meaning beyond the schema's parameter descriptions. Both parameters are well-documented in the schema itself.
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 'List' and resource 'weather observation stations' with a specific geographic scope 'for a US state'. This distinguishes it from siblings like get_alerts or get_forecast.
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 listing stations by state, but provides no explicit guidance on when to use this vs alternatives like get_alerts or get_forecast. There are no exclusions or prerequisites mentioned.
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?
No annotations provided, but description discloses rate limits and content constraints well. It doesn't detail confidentiality or response expectations, but this is reasonable 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?
Description is three sentences, front-loaded with purpose, and all information is relevant without redundancy. Efficient use of space.
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 tool (no output schema, few params) the description covers purpose, usage, constraints, and rate limits. Lacks details on feedback confirmation or privacy, but generally sufficient.
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 description adds limited value beyond schema. The message constraint about not including user prompt is additional, but the schema already details parameter enums and structure.
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's purpose: sending feedback to the Pipeworx team, listing specific categories (bug reports, feature requests, etc.). It distinguishes itself from sibling tools by being the only feedback 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?
Provides explicit guidance on when to use (for feedback), what to include (describe what you tried, avoid user's prompt verbatim), and rate limits (5/day). Sibling tools are unrelated, so no confusion.
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?
No annotations provided, so description carries full burden. It mentions retrieval and listing, but does not disclose if retrieval is destructive or has any side effects. However, given the simple read nature, this is adequate but not exceptional.
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, front-loaded with action and conditions. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no required params and no output schema, the description is complete enough. It explains the two use cases and hints at the tool's role in cross-session memory. Could be improved by noting that memories are persistent, but not required.
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% (one parameter 'key' with description). The description adds context that omitting key lists all memories, which aligns with schema's 'omit to list all keys' but is essentially restating. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool retrieves a memory by key or lists all memories, distinguishing between two modes. The verb 'retrieve' and resource 'memory' are specific and distinct from siblings like 'remember' 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?
Explicitly states when to omit key (to list all) and when to provide key (to retrieve specific memory). Provides context on retrieving 'context you saved earlier', which guides usage relative to session history.
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?
The description explains the parallel fan-out to three sources, the return structure (structured changes, count, URIs), and parameter formats. While it lacks edge case details (e.g., behavior with no changes), it sufficiently discloses behavior beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: purpose, source details, parameter format, return values, usage guidance. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity and no output schema, the description covers key aspects: sources, return fields, parameter usage. It could benefit from more detail on the response structure or pagination, but it is largely complete for the tool's scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds value by explaining the accepted formats for 'since' (ISO date or relative) and 'value' (ticker or CIK). It also clarifies the enum constraint on 'type'.
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: 'What's new about an entity since a given point in time.' It specifies the entity type (company) and the data sources (SEC EDGAR, GDELT, USPTO), distinguishing it from sibling tools like entity_profile or compare_entities.
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 states usage: 'Use for "brief me on what happened with X" or change-monitoring workflows.' This provides clear context, though it does not mention when not to use it or explicitly list alternatives.
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?
Discloses behavioral traits such as persistence behavior (authenticated vs anonymous sessions) and purpose. With no annotations provided, the description carries the full burden and does well, though it could mention potential overwrite behavior or storage limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise two-sentence description. First sentence defines purpose, second adds context on persistence. 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?
Given low complexity (2 parameters, no nested objects, no output schema), the description is nearly complete. Could optionally mention overwrite behavior or memory limits, but not essential.
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% and the description adds context beyond schema by explaining what to store (findings, preferences, notes) but does not add meaning 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?
Description clearly states the verb ('store') and resource ('key-value pair in session memory'), and distinguishes from sibling tools like 'recall' and 'forget' by explicitly mentioning memory persistence and session context.
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 ('save intermediate findings, user preferences, or context across tool calls') and provides persistence differences (authenticated vs anonymous). However, no explicit mention of when not to use or alternatives.
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 are present, so the description must cover behavior. It discloses the operation is a single call and lists return values, but does not discuss failure modes, rate limits, or idempotency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences with no wasted words. It front-loads the purpose and then efficiently covers input, output, and value proposition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains the return values. It also notes the efficiency benefit. Could mention error handling for completeness, but overall satisfactory.
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 the description adds value by providing specific examples (AAPL, 0000320193, Apple) and noting version support, which goes beyond the schema's simple type and enum.
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 an entity to canonical IDs and provides specific details for v1 (company type). It is a specific verb+resource combination, but does not explicitly differentiate itself from sibling tools, though it implies it replaces multiple lookup 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?
The description indicates when to use the tool (when needing canonical IDs) and provides accepted input formats, but lacks explicit when-not-to-use scenarios or alternatives.
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?
With no annotations, the description must disclose behavioral traits. It explains the output (verdict, structured form, value, citation, delta) and that it makes external calls to SEC EDGAR. However, it omits potential failure modes, error handling, rate limits, or permission requirements. The claim that it replaces 4-6 calls hints at efficiency but lacks detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, using only three sentences to convey purpose, scope, version, output, and efficiency benefit. It front-loads the core action and avoids extraneous detail, making it quick to read and understand.
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 a single parameter and no output schema, the description fully covers what the tool does and returns. It describes the verdict types, extracted form, actual value with citation, and percent delta. It also provides context about supported sources and versioning, making it complete for the intended use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with a description for the single 'claim' parameter. The tool description adds value by specifying the scope ('company-financial claims') and providing examples. This clarifies acceptable inputs beyond the schema's generic description, justifying a score above baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: fact-check a natural-language claim against authoritative sources for company-financial claims. It specifies supported domains (revenue, net income, cash for US public companies) and data sources (SEC EDGAR + XBRL). It also distinguishes from siblings by noting it replaces multiple sequential agent calls, making its function unique.
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 context on when to use the tool: for fact-checking specific company-financial claims. It implies limitations by including 'v1 supports' and names the types of claims. However, it does not explicitly state when not to use or list alternatives, which would improve guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
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