onthisday
Server Details
On This Day MCP — wraps byabbe.se/on-this-day (free, no auth)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-onthisday
- GitHub Stars
- 0
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Tool access control
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Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.9/5 across 11 of 11 tools scored. Lowest: 2.9/5.
Most tools have distinct purposes: historical date lookups (get_births, get_deaths, get_events) are clear, and utility tools (memory, feedback, entity resolution) are separate. However, ask_pipeworx could be ambiguous as it claims to pick the right tool, potentially causing confusion with direct tool use.
Naming patterns vary: some tools follow verb_noun (ask_pipeworx, discover_tools, resolve_entity), while historical tools use get_ prefix. Memory tools are bare verbs (forget, remember, recall) and pipeworx_feedback uses noun_noun, showing inconsistency.
11 tools is a reasonable number, balancing domain-specific historical lookups with general utility tools. The count is well-scoped for the combined functionality, though slightly high if considering only the historical domain.
The historical domain covers births, deaths, and events for a date, which is basic. Missing common categories like holidays or discoveries. The inclusion of many Pipeworx-platform tools dilutes the domain focus, leaving gaps in historical coverage.
Available Tools
11 toolsask_pipeworxAInspect
Ask a question in plain English and get an answer from the best available data source. Pipeworx picks the right tool, fills the arguments, and returns the result. No need to browse tools or learn schemas — just describe what you need. Examples: "What is the US trade deficit with China?", "Look up adverse events for ozempic", "Get Apple's latest 10-K filing".
| 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?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool picks the right data source, fills arguments automatically, and returns results. However, it lacks details on limitations (e.g., data source availability, error handling, or rate limits), which are important for a tool with such broad functionality.
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 front-loaded with the core functionality, followed by supporting details and examples. Every sentence earns its place by clarifying usage, differentiating from alternatives, and illustrating with examples, with no wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (natural language querying with automatic tool selection) and no output schema, the description does well by explaining the process and providing examples. However, it could be more complete by mentioning potential limitations or the types of data sources available, which would help set user expectations more fully.
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 value by explaining the parameter's purpose beyond the schema's 'natural language' note: it emphasizes 'plain English' questions and provides examples like 'Look up adverse events for ozempic,' giving concrete context for how to formulate the question parameter effectively.
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: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and distinguishes itself from siblings by emphasizing natural language interaction without needing to browse tools or learn schemas.
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 when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives by implication (use other tools for structured queries) and includes concrete examples like 'What is the US trade deficit with China?' to illustrate appropriate use cases.
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 entities side by side in one call. type="company": revenue, net income, cash, long-term debt from SEC EDGAR. type="drug": adverse-event report count, FDA approval count, active trial count. Returns paired data + pipeworx:// resource URIs. Replaces 8–15 sequential agent calls.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type: "company" or "drug". | |
| values | Yes | For company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It states that the tool returns 'paired data + pipeworx:// resource URIs' but does not mention any side effects, authentication needs, or limitations. For a read-only comparison tool, this is adequate but lacks detail on error handling or data structure.
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 at two sentences. It starts with the core purpose, then immediately details the data returned for each type. Every sentence adds unique value, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (two parameters, no output schema), the description covers the essential aspects: purpose, input rules, and output summary. It would benefit from clarifying the structure of the 'paired data' output, but it is sufficient for an AI agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage, describing both parameters. The description adds value by explaining the meaning of each entity type and providing examples for the 'values' parameter (e.g., tickers for company, drug names). This helps the agent understand how to construct valid inputs beyond what the schema alone provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: comparing 2–5 entities side by side. It specifies two entity types (company and drug) with distinct data fields, and uses a specific verb ('compare') and resource ('entities'). It effectively distinguishes itself from sibling tools, which do not perform comparisons.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains that the tool replaces 8–15 sequential agent calls, implying it should be used when comparing multiple entities simultaneously. While it does not explicitly state when not to use it or mention alternative tools, the context is clear enough for an AI agent to understand its primary use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discover_toolsAInspect
Search the Pipeworx tool catalog by describing what you need. Returns the most relevant tools with names and descriptions. Call this FIRST when you have 500+ tools available and need to find the right ones for your task.
| 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 are provided, so the description carries the full burden of behavioral disclosure. It describes the search functionality and return format (tools with names and descriptions), but lacks details on behavioral traits like rate limits, authentication needs, or error handling. For a tool with no annotations, this is a moderate gap, as it covers the core operation but misses operational constraints.
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, with two sentences that each serve a clear purpose: the first explains the tool's function, and the second provides usage guidance. There is no wasted text, and key information is front-loaded, making it easy for an agent to quickly understand the tool's role.
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 complexity (a search tool with 2 parameters, no output schema, and no annotations), the description is mostly complete. It explains the purpose, usage context, and return format, but lacks details on output structure or behavioral constraints. Since there is no output schema, the description could benefit from more on return values, but it's adequate for basic understanding.
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 schema already documents both parameters (query and limit) thoroughly. The description does not add any parameter-specific semantics beyond what the schema provides, such as examples or usage tips. Baseline 3 is appropriate when the schema does the heavy lifting, but no extra value is added.
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 with a specific verb ('Search') and resource ('Pipeworx tool catalog'), and it distinguishes itself from siblings by focusing on discovery rather than data retrieval like get_births, get_deaths, and get_events. It explicitly mentions returning relevant tools with names and descriptions, which is distinct from the sibling tools that likely fetch specific data types.
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 usage guidelines: it specifies when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task') and implies when not to use it (e.g., for direct data retrieval tasks handled by siblings). It clearly positions this as a discovery tool for large catalogs, offering a clear alternative to manually browsing or guessing among many tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetBInspect
Delete a stored memory by key.
| 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 'Delete' which implies a destructive mutation, but doesn't disclose behavioral traits like whether deletion is permanent, requires specific permissions, returns confirmation, or handles errors (e.g., invalid keys). This leaves significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly without unnecessary elaboration.
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 destructive mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., error handling, permanence) and output expectations, which are critical for safe and effective tool invocation in this 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?
The schema description coverage is 100%, with the parameter 'key' documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format or examples. With high schema coverage, the baseline is 3, as the schema does the heavy lifting.
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 a specific action (delete) on a specific resource (stored memory) using a specific identifier (key). It distinguishes from siblings like 'recall' (retrieve) and 'remember' (store), establishing a clear purpose.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing memory key), exclusions (e.g., what happens if the key doesn't exist), or comparisons to sibling tools like 'recall' for retrieval or 'remember' for storage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_birthsBInspect
Find notable people born on a specific date. Provide month (1-12) and day (1-31). Returns names, birth years, and biographical details.
| Name | Required | Description | Default |
|---|---|---|---|
| day | Yes | Day of the month (1-31). | |
| month | Yes | Month as a number (1-12). |
Output Schema
| Name | Required | Description |
|---|---|---|
| date | Yes | The date string from the API response |
| type | Yes | The type of entries returned |
| count | Yes | Number of births found |
| births | Yes | Array of notable births |
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 of behavioral disclosure. It states it returns a list but doesn't describe format, pagination, rate limits, authentication needs, or error conditions. For a read operation with zero annotation coverage, this leaves significant 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, efficient sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a simple lookup 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?
For a simple read tool with two well-documented parameters and no output schema, the description is adequate but incomplete. It lacks behavioral details (e.g., response format, error handling) that would help an agent use it correctly, especially given the absence of annotations.
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 description adds minimal value beyond the input schema, which has 100% coverage. It mentions 'specific month and day' but doesn't explain parameter interactions or constraints beyond what the schema already documents (e.g., month 1-12, day 1-31). Baseline 3 is appropriate when the schema does most of the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get a list'), resource ('notable people born'), and scope ('on a specific month and day across all years'). It distinguishes from sibling tools like 'get_deaths' and 'get_events' by focusing exclusively on births rather than deaths or events.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools, prerequisites, or any contextual constraints. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_deathsCInspect
Find notable people who died on a specific date. Provide month (1-12) and day (1-31). Returns names, death years, and biographical detail.
| Name | Required | Description | Default |
|---|---|---|---|
| day | Yes | Day of the month (1-31). | |
| month | Yes | Month as a number (1-12). |
Output Schema
| Name | Required | Description |
|---|---|---|
| date | Yes | The date string from the API response |
| type | Yes | The type of entries returned |
| count | Yes | Number of deaths found |
| deaths | Yes | Array of notable deaths |
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 of behavioral disclosure. It describes a read operation ('Get a list'), implying it is non-destructive, but does not address other behavioral traits such as rate limits, authentication needs, error handling, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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, well-structured sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the core action and includes essential details like scope ('across all years'), making it easy to parse. Every part of the sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for effective tool use. It does not explain what the return value includes (e.g., list structure, fields like names or years), potential limitations (e.g., data source, pagination), or error conditions. For a tool with no structured output information, the description should provide more context to compensate.
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% description coverage, with clear documentation for both parameters ('month' and 'day'), including their types and valid ranges. The description adds no additional parameter semantics beyond what the schema provides, such as format details or usage examples. According to the rules, with high schema coverage (>80%), the baseline score is 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get a list of notable people who died on a specific month and day across all years.' It specifies the verb ('Get'), resource ('list of notable people'), and scope ('across all years'), making it easy to understand. However, it does not explicitly differentiate from sibling tools like 'get_births' or 'get_events' beyond the focus on deaths, which is implied but not stated.
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 no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'get_births' or 'get_events' for comparison, nor does it specify any prerequisites, exclusions, or contextual cues for usage. The agent must infer usage based on the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_eventsBInspect
Search historical events on a specific date. Provide month (1-12) and day (1-31). Returns event descriptions, years, and details.
| Name | Required | Description | Default |
|---|---|---|---|
| day | Yes | Day of the month (1-31). | |
| month | Yes | Month as a number (1-12). |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes a read-only operation ('Get a list'), but lacks details on permissions, rate limits, pagination, or response format. The absence of annotations means critical behavioral traits are undocumented, leaving gaps for an AI agent.
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, efficient sentence that front-loads the purpose without unnecessary words. It directly conveys the tool's function and scope, making it easy to parse and understand quickly.
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 annotations), the description is adequate but incomplete. It covers the basic purpose and parameters but lacks behavioral details (e.g., response structure, error handling), which are needed for full contextual understanding in the absence of annotations.
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% description coverage, fully documenting both parameters (month and day) with their types and ranges. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or edge cases, so it meets the baseline for high schema 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?
The description clearly states the verb ('Get') and resource ('list of historical events'), specifying the scope ('on a specific month and day across all years'). It distinguishes from siblings by focusing on general events rather than births or deaths, though it doesn't explicitly name the alternatives.
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 context by specifying 'historical events on a specific month and day,' suggesting it's for date-based queries. However, it doesn't provide explicit guidance on when to use this versus the sibling tools (get_births, get_deaths), nor does it mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pipeworx_feedbackAInspect
Send feedback to the Pipeworx team. Use for bug reports, feature requests, missing data, or praise. Describe what you tried in terms of Pipeworx tools/data — do not include the end-user's prompt verbatim. Rate-limited to 5 messages per identifier per day. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | bug = something broke or returned wrong data. feature = a new tool or capability you wish existed. data_gap = data Pipeworx does not currently expose. praise = positive note. other = anything else. | |
| context | No | Optional structured context: which tool, pack, or vertical this relates to. | |
| message | Yes | Your feedback in plain text. Be specific (which tool, what error, what data was missing). 1-2 sentences typical, 2000 chars max. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers behavioral traits: rate limiting, intended content, and that it's free. Does not detail response or privacy, but sufficient for a feedback tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise: two sentences plus rate limit note. Front-loaded with purpose and use cases. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple feedback tool with no output schema, description covers purpose, usage, constraints, and parameter details thoroughly. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage, but description adds value by explaining enum choices in plain language and giving format guidance (e.g., '1-2 sentences typical, 2000 chars max') and context 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 clearly states the tool's purpose: 'Send feedback to the Pipeworx team' and lists specific use cases (bug reports, feature requests, etc.), distinguishing it from sibling tools that are about data retrieval or memory.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidelines: what to include (describe in terms of Pipeworx tools/data), what not to include (end-user prompt verbatim), and a rate limit (5 per day). Lacks explicit comparison to siblings, but purpose is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recallAInspect
Retrieve a previously stored memory by key, or list all stored memories (omit key). Use this to retrieve context you saved earlier in the session or in previous sessions.
| Name | Required | Description | Default |
|---|---|---|---|
| key | No | Memory key to retrieve (omit to list all keys) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adequately describes the core behavior (retrieval/listing) and persistence across sessions ('in previous sessions'), but lacks details about error handling (e.g., what happens if key doesn't exist), return format, or any limitations like size constraints or access controls. The description doesn't contradict annotations since none exist.
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 perfectly concise and well-structured in two sentences. The first sentence front-loads the core functionality with clear alternatives. The second sentence adds important context about session persistence without redundancy. Every word earns its place, and there's no wasted text.
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 moderate complexity (retrieval with optional listing), no annotations, and no output schema, the description is adequate but has gaps. It covers the basic purpose and usage well, but lacks information about return values, error conditions, or any behavioral constraints. For a memory retrieval tool that might return structured data, more detail about output would be helpful.
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 description coverage is 100%, so the parameter 'key' is already documented in the schema. The description adds valuable semantic context by explaining the dual behavior: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' This clarifies that omitting the parameter triggers a different operation, which isn't obvious from the schema alone. With only one optional parameter, this provides good compensation.
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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations. The phrase 'context you saved earlier' further clarifies the relationship with storage tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It clearly defines the two usage modes and specifies the condition for switching between them. The context 'Use this to retrieve context you saved earlier' reinforces its role versus storage tools like 'remember'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rememberAInspect
Store a key-value pair in your session memory. Use this to save intermediate findings, user preferences, or context across tool calls. Authenticated users get persistent memory; anonymous sessions last 24 hours.
| 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?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it explains persistence differences (authenticated users get persistent memory, anonymous sessions last 24 hours) and the cross-tool context capability. However, it doesn't cover potential limitations like storage size or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences that are front-loaded and earn their place: the first states the core purpose, the second adds crucial behavioral context about persistence. Zero waste or 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 tool's moderate complexity (storage with persistence nuances), no annotations, and no output schema, the description is mostly complete. It covers purpose, usage context, and key behavioral traits, but lacks details on return values or error conditions, which would be helpful since there's no output schema.
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 schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, but doesn't need to compensate for gaps. Baseline 3 is appropriate when the schema does the heavy lifting.
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 specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from siblings like 'recall' (retrieve) and 'forget' (remove). It specifies the purpose is for saving intermediate findings, user preferences, or context across tool 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 provides clear context on when to use this tool (to save intermediate findings, user preferences, or context across tool calls), but does not explicitly state when not to use it or name alternatives. It implies usage vs. 'recall' for retrieval, but lacks explicit exclusions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resolve_entityAInspect
Resolve an entity to canonical IDs across Pipeworx data sources in a single call. Supports type="company" (ticker/CIK/name → SEC EDGAR identity) and type="drug" (brand or generic name → RxCUI + ingredient + brand). Returns IDs and pipeworx:// resource URIs for stable citation. 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 provided, so the description must cover behavioral traits. It discloses output fields (ticker, CIK, name, resource URIs) and input types, but does not mention error handling or ambiguous matches, leaving some 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?
Two sentences, front-loaded with the core purpose, and no extraneous words. Each sentence adds essential information efficiently.
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 parameters and no output schema, the description adequately covers input formats, output fields, and the benefit (replaces multiple calls). Minor omission is lack of error handling details, but overall sufficient for agent decision-making.
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 both parameters with 100% description coverage. The description adds value by giving concrete examples (AAPL, 0000320193, Apple) and explaining the three acceptable forms for the value parameter, going beyond the schema's enums and descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool resolves entities to canonical IDs across Pipeworx data sources, which is a specific verb+resource. It distinguishes from siblings like ask_pipeworx or get_events by focusing on entity ID resolution.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions it replaces 2–3 lookup calls, implying efficiency gains. It clearly specifies input formats (ticker, CIK, name) and notes that v1 only supports company type, providing clear context for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
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Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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