Clickup
Server Details
ClickUp MCP — wraps the ClickUp REST API v2 (BYO API key)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-clickup
- GitHub Stars
- 0
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Usage analytics
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Tool Definition Quality
Average 4/5 across 16 of 16 tools scored. Lowest: 3.2/5.
Tools are mostly distinct by domain, but 'ask_pipeworx' serves as a meta-tool that can replicate many other tools' functions, creating ambiguity. Additionally, 'compare_entities', 'entity_profile', and 'recent_changes' overlap in entity data queries, though descriptions help distinguish them.
Naming conventions are mixed: ClickUp tools use 'clickup_verb_noun', Pipeworx tools use various patterns (verb_noun, noun_adjective, single verb), and memory tools use bare verbs. No consistent prefix or verb structure across the set.
16 tools is a moderate count, but the server bundles two distinct domains (ClickUp and Pipeworx) under one name, making it feel cluttered. A more focused scope would be appropriate for a server named 'Clickup'.
The Pipeworx data query tools are fairly comprehensive, but the ClickUp subset is incomplete (missing update and delete for tasks, no folder/space management). Memory tools are complete. Overall, notable gaps in the ClickUp lifecycle.
Available Tools
16 toolsask_pipeworxARead-onlyInspect
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".
| 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?
The description reveals that the tool picks the right tool and fills arguments, providing insight into its internal behavior. Since no annotations are provided, this adds necessary transparency about its autonomous nature.
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, using three short sentences plus examples. It is front-loaded with the purpose, and every sentence contributes meaningful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter and lack of output schema, the description is complete enough. It explains the tool's autonomous selection behavior and provides examples. A minor gap is not specifying what happens if the question cannot be answered.
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 context by explaining the parameter as 'Your question or request in natural language' and providing examples. This adds value beyond the schema, but the schema already adequately describes the 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 clearly states that the tool answers natural language questions by selecting the best data source, with specific examples like 'What is the US trade deficit with China?'. This distinguishes it from sibling tools like clickup_create_task or discover_tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'No need to browse tools or learn schemas' and gives examples, implying when to use it. However, it does not explicitly state when not to use it or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
clickup_create_taskARead-onlyInspect
Create a new task in a ClickUp list. Provide list ID, task name, and optionally priority and assignee. Returns task ID, name, status, and URL.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Task name | |
| _apiKey | Yes | ClickUp API token | |
| list_id | Yes | List ID to create the task in | |
| due_date | No | Due date as Unix timestamp in milliseconds | |
| priority | No | Priority: 1 (urgent), 2 (high), 3 (normal), 4 (low) | |
| description | No | Task description (markdown supported) |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description clarifies it creates a task (mutation) and returns specific fields. However, lacks details on authentication requirements or side effects beyond creation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no redundancy. First sentence states action and result, second sentence lists return fields. Efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and 6 parameters, description covers basic return fields but misses details like whether due_date is optional, expected response shape, or error handling. Adequate but not exhaustive.
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 covers 100% of parameters with descriptions. Description adds no additional meaning beyond schema, e.g., does not explain date format or priority enum values beyond what schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Create', resource 'task in a ClickUp list', and explicitly lists return values (ID, name, status, URL). Distinguishes from sibling tools like 'clickup_get_task' which retrieves tasks.
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?
Implied usage from description but no explicit guidance on when to use vs. alternatives like clickup_list_tasks. 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.
clickup_get_taskARead-onlyInspect
Fetch full task details including name, description, status, priority, assignees, tags, and time tracking. Provide task ID (e.g., "9hz6c").
| Name | Required | Description | Default |
|---|---|---|---|
| _apiKey | Yes | ClickUp API token | |
| task_id | Yes | Task ID |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool returns full task details and lists the included fields, which is useful behavioral context. Since no annotations are provided, the description carries the full burden, and it adequately describes the read-only nature and scope of data returned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, zero waste. The first sentence clearly states the action and identifier, and the second summarizes the return fields. Every word is functional.
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 (two simple parameters, no nested objects, no output schema), the description is complete enough. It covers what the tool does and what it returns. No additional context is necessary for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description does not add meaning beyond what the schema provides for '_apiKey' and 'task_id'. It mentions returning details but does not elaborate on parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Get' and resource 'a single ClickUp task by ID'. It distinguishes from siblings like 'clickup_list_tasks' (which returns multiple tasks) and 'clickup_create_task' (which creates). The details listed (name, description, status, etc.) clarify the return content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For example, it doesn't mention that this tool is for retrieving a specific task by ID, while 'clickup_list_tasks' is for querying tasks in a list. The description implies usage but lacks explicit when-to-use or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
clickup_list_foldersBRead-onlyInspect
List all folders in a ClickUp space. Provide space ID (e.g., "789"). Returns folder ID, name, and list count.
| Name | Required | Description | Default |
|---|---|---|---|
| _apiKey | Yes | ClickUp API token | |
| space_id | Yes | Space ID |
Output Schema
| Name | Required | Description |
|---|---|---|
| folders | Yes | Array of folder objects |
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 reveals that it returns folder ID, name, and list count, but does not disclose any side effects, error conditions, or pagination behavior. Adequate for a simple read-only list operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no wasted words. Information is front-loaded. Could potentially be more structured, but efficient.
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 is a simple list operation with two well-documented parameters and no output schema, the description is mostly complete. However, it lacks details on output format or pagination, which would be helpful for agents.
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 parameters are already documented. The description does not add additional meaning beyond what the schema provides, which is acceptable given high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it lists folders in a ClickUp space, and specifies the returned fields (ID, name, list count). It distinguishes from siblings like clickup_list_tasks and clickup_list_spaces by focusing on folders.
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?
Implied usage for listing folders given a space_id, but no explicit guidance on when to use this versus other list tools (e.g., when to list folders vs tasks vs spaces) 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.
clickup_list_spacesBRead-onlyInspect
List all spaces in your ClickUp workspace. Returns space ID, name, and status.
| Name | Required | Description | Default |
|---|---|---|---|
| _apiKey | Yes | ClickUp API token | |
| team_id | Yes | Team/workspace ID |
Output Schema
| Name | Required | Description |
|---|---|---|
| spaces | Yes | Array of space objects |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are empty, so the description carries full burden. It states it is a read operation ('List') and provides output fields (space ID, name, status info). However, it does not mention pagination, rate limits, or any authorization requirements beyond the API key.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that efficiently conveys purpose and output. It is front-loaded with the action and resource.
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 nested objects, no output schema), the description is mostly complete. However, it could mention that spaces are top-level containers and that team_id is required. The lack of output schema means description could clarify return format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the schema. It does not explain what team_id represents or how to obtain it, but the schema's description is sufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists spaces in a ClickUp team/workspace and returns space ID, name, and status info. However, it does not explicitly distinguish itself from sibling tools like clickup_list_folders or clickup_list_tasks, which might be useful for disambiguation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. For instance, if the agent wants to list folders or tasks, it might need to use different tools, but no such distinction is made.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
clickup_list_tasksBRead-onlyInspect
List all tasks in a ClickUp list. Returns task ID, name, status, priority, assignees, due date, and URL. Provide list ID (e.g., "123456").
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number (0-indexed, default 0) | |
| _apiKey | Yes | ClickUp API token | |
| list_id | Yes | List ID to fetch tasks from |
Output Schema
| Name | Required | Description |
|---|---|---|
| tasks | Yes | Array of task objects |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description mentions returned fields but does not disclose pagination behavior (page parameter is described in schema), rate limits, or authentication requirements. No annotations provided, so description carries full burden; it is 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?
Two concise sentences front-load purpose and output fields. No wasted words; could optionally include usage notes without sacrificing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description partially compensates by listing return fields. However, lacks pagination details, filtering options, or error handling info. Adequate for a straightforward list endpoint but could be more 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 baseline is 3. Description adds no extra meaning beyond schema for page, _apiKey, and list_id; it correctly omits redundancy.
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 lists tasks in a ClickUp list and enumerates returned fields (task ID, name, status, etc.). Distinguishes from sibling tools like clickup_create_task and clickup_get_task by focusing on listing, though not explicitly differentiating from other list tools like clickup_list_folders.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. Does not mention that it returns tasks from a single list, nor when to use clickup_get_task for a specific task. Lacks usage context such as filtering or sorting capabilities.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_entitiesARead-onlyInspect
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?
With no annotations, the description carries the full burden. It discloses the type of data returned (financial metrics, counts, URIs) and implies a read-only operation. However, it does not explicitly state read-only or mention any side effects, leaving slight ambiguity.
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, front-loaded with the core purpose, then adding details. Every sentence is essential and no fluff, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description should more fully explain the return format. It only mentions 'paired data' and URIs, but does not specify the structure (e.g., JSON format, field names). This leaves some ambiguity for the agent on how to parse the response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides good descriptions for both parameters. The description adds value by giving examples of data fields returned per entity type and examples of input values, enhancing clarity beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: compare 2-5 entities side by side in one call. It specifies two entity types and the exact data fields returned for each, making it highly specific and distinguishable from other tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool by stating it replaces 8-15 sequential agent calls, suggesting efficiency. However, it does not explicitly name alternative tools or state when not to use it, which would make it score a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discover_toolsARead-onlyInspect
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?
No annotations provided, so the description must fully disclose behavior. It states the tool returns 'most relevant tools with names and descriptions', but does not mention whether it is read-only, destructive, or has any side effects. The description is neutral and does not contradict annotations (none), but it lacks explicit behavioral traits beyond the search result. Since there are no annotations, a score of 3 is appropriate: it gives basic behavioral info but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, each serving a purpose: what it does, what it returns, and when to call it. No extraneous words. Slightly more verbose than strictly necessary, but the instruction 'Call this FIRST' earns its place. Score 4 for being efficient but not maximally 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?
Given the tool's simple search functionality, 2 parameters, no output schema, and no annotations, the description covers the core purpose and usage context. It explains when to use it (first, with many tools). It does not describe the output format (e.g., whether it includes tool IDs or just names), but the tool is straightforward and the description is sufficient for an agent to decide when to call it. A score of 4 reflects good completeness for this complexity level.
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%: both parameters ('query' and 'limit') have descriptions in the schema. The description adds no extra parameter-level meaning beyond the schema. Baseline 3 is appropriate because the schema already handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verb 'Search' and resource 'Pipeworx tool catalog', clearly stating the tool's function: to find relevant tools by natural language description. It distinguishes itself from sibling tools (which are about ClickUp, memory, or AI chat) by being the discovery/search tool for the tool catalog.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
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 (before other tools) and the context (large tool set). No sibling alternatives are mentioned, but the call-to-action is strong and unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
entity_profileARead-onlyInspect
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 are provided, so the description carries full burden. It details what data is returned (SEC filings, revenue, patents, news, LEI) and the output format (pipeworx:// citation URIs). However, it does not mention potential errors, rate limits, or authorization requirements, preventing a score of 5.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose, and every sentence adds unique value. No redundant or extraneous information. Excellent conciseness.
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 tool with two simple parameters, no output schema, and no annotations, the description provides complete context: what the tool does, what data it returns, how to use the parameters, and when to use alternatives. The agent has sufficient information to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds significant context beyond the schema: it explains that type is currently only 'company', gives examples for value (ticker or CIK), and clarifies that names are not supported, directing to resolve_entity. This justifies a score of 4.
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 returns a full profile of an entity across multiple packs, listing specific data types (SEC filings, revenue, patents, news, LEI) and distinguishing itself from sibling tools like resolve_entity and usa_recipient_profile. The verb is specific and the resource is well-defined.
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 explicit guidance: when to use (for comprehensive entity profiles, replacing 10-15 calls), when not to use (for federal contracts, use usa_recipient_profile), and how to prepare input (use resolve_entity for names). This is highly actionable for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetADestructiveInspect
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 exist, so the description carries full burden. It correctly describes a destructive action (delete) but does not disclose consequences (e.g., irreversible? requires auth?). The description is accurate but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no waste. Front-loaded with verb and resource. Ideal length for a simple tool.
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 is simple (1 param, no output schema). The description fully captures the purpose and parameter. Additional context like auth requirements or side effects would be nice but not essential given the tool's trivial nature.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one parameter 'key'. The schema's description 'Memory key to delete' already explains it. The tool description adds no extra meaning, but given full coverage, the baseline is 3. However, the description clarifies the action context (delete), earning a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear verb 'Delete' with a specific resource 'stored memory by key'. It uniquely identifies the action and distinguishes from siblings like 'recall' (read) and 'remember' (create).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states what it does but provides no guidance on when to use it vs alternatives. Sibling tools like 'recall' and 'remember' suggest a memory management context, but no explicit exclusions or prerequisites are given.
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?
Discloses rate-limiting and free usage, which are key behavioral traits. No annotations provided, so description carries full burden. Could mention response behavior (e.g., 'we'll review it'). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Very concise, single paragraph. Purpose is front-loaded. Every sentence is essential, 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 feedback tool, the description is complete: purpose, usage, rate limits, content guidelines. No output schema needed, nested object context optional.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage, so baseline is 3. The description does not add significant extra meaning beyond schema's parameter descriptions. Adequate.
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: sending feedback to the Pipeworx team. It lists specific use cases (bug reports, feature requests, missing data, praise) and distinguishes itself from sibling tools like ask_pipeworx.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear when-to-use context and explicit guidelines on content (describe what was tried in terms of Pipeworx tools/data, avoid including end-user prompt verbatim). Mentions rate limit of 5 per identifier per day. Lacks explicit alternatives, but this is a niche feedback tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recallARead-onlyInspect
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 discloses the dual behavior (retrieve by key or list all) and mentions persistence across sessions, but does not detail what happens if key is missing, or any error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that front-load the core purpose. Efficient and clear.
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 retrieval tool with no required parameters and no output schema, the description sufficiently explains behavior. It mentions session persistence which adds valuable 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 description coverage is 100%, so baseline is 3. The description adds the detail that omitting key lists all memories, which is already implied by optionality in schema. No additional parameter info needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool retrieves a memory by key or lists all memories when key is omitted, which distinguishes it 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says when to use the tool ('retrieve context you saved earlier') and implies when not to (omit key to list all), but does not explicitly mention alternatives 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.
recent_changesARead-onlyInspect
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?
No annotations exist, so description carries full burden. It transparently explains the parallel fan-out to three sources, output format (changes, count, URIs), and parameter formats. It does not disclose any side effects or rate limits, but the behavior is well-described.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three focused sentences: purpose, behavior (fan-out), and parameter details. Every sentence adds value; no filler. Front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description covers output structure (structured changes, total_changes, URIs) and sources. Lacks details on error handling or rate limits but is sufficient for understanding main behavior.
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 clear param descriptions. The tool description reiterates some param info (since formats, type enum) but adds little new meaning beyond schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: reporting what's new about an entity since a time. It specifies the fan-out for 'company' and gives explicit use cases ('brief me on what happened', 'change-monitoring'), distinguishing it from siblings like 'entity_profile'.
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 usage context ('use for change-monitoring') but does not explicitly exclude alternatives or state when not to use. It implies use for change monitoring, not full profile, which is good but lacks exclusions.
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?
Description adds behavioral context beyond what annotations provide (none here): authenticated users get persistent memory, anonymous sessions last 24 hours. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with action and use cases. Efficient and informative, though could be slightly more 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?
Given no output schema and only 2 parameters, the description is complete enough: explains persistence behavior, use cases, and key-value nature. No missing context for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so description adds little beyond schema. It gives example keys like 'subject_property' and clarifies value as 'any text', but these are also present in schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Store a key-value pair in your session memory', with specific verb 'store' and resource 'session memory'. It distinguishes itself from siblings (recall, forget) by explicitly mentioning memory persistence and use cases.
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 use cases: 'save intermediate findings, user preferences, or context across tool calls'. However, it does not explicitly state when not to use or compare to siblings like recall or forget.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resolve_entityARead-onlyInspect
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?
With no annotations, the description carries full burden. It discloses return values (ticker, CIK, name, URIs), version limitations (company only), and performance benefit (replaces 2-3 calls). Could mention idempotency but acceptable for a lookup 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?
Three sentences, front-loaded with purpose, each sentence adds new information (purpose, examples/return, benefit). No redundancy or fluff; every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description adequately explains return values. It covers versioning and input options. Lacks error handling info but is complete for a simple lookup tool in the context of Pipeworx siblings like ask_pipeworx.
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 description adds value by providing concrete examples (AAPL, 0000320193, Apple) and clarifying accepted formats beyond the terse schema descriptions. The enum for type is reinforced.
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 (resolve), resource (entity), and outcome (canonical IDs across Pipeworx data sources). It distinguishes itself from siblings by emphasizing it replaces multiple lookup calls and specifies version 1 scope.
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 for when to use (single call for canonical IDs) and gives examples of acceptable inputs. However, it lacks explicit guidance on when not to use it or alternatives like ask_pipeworx for general queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_claimARead-onlyInspect
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?
Without annotations, description details the return value (verdict, structured form, actual value with citation, percent delta). It does not mention potential limitations like data availability or rate limits, but for a read-like fact-checking tool this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences, front-loaded with the core purpose, and every sentence adds essential information including scope, 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 one parameter, no output schema, no annotations, the description is remarkably complete: it explains input, output, supported domains, data sources, and comparative advantage over alternatives.
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?
Single parameter 'claim' has a clear description with examples (100% schema coverage). Description adds value by providing concrete examples that clarify format beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fact-checks natural-language claims using authoritative sources (SEC EDGAR + XBRL) for company-financial data, lists possible verdicts, and distinguishes itself from sibling tools by replacing multiple sequential calls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly specifies supported claim types (company-financial, US public companies) and provides examples. Implies when not to use (non-financial or non-US claims) and frames it as a replacement for alternative multi-step approaches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
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