nationalize
Server Details
Nationalize MCP — nationality prediction from first name (nationalize.io, free, no auth)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-nationalize
- GitHub Stars
- 0
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
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 4.1/5 across 8 of 8 tools scored. Lowest: 2.9/5.
Most tools have distinct purposes: nationality prediction (predict_nationality, batch_predict), memory (remember, recall, forget), and Pipeworx utilities (ask_pipeworx, discover_tools, resolve_entity). However, ask_pipeworx could subsume other tools, causing slight ambiguity.
All tool names use snake_case and mostly follow a verb_noun pattern (e.g., predict_nationality, resolve_entity). The memory tools (forget, recall, remember) are single verbs, which is a minor inconsistency but still readable.
8 tools is a reasonable count for a server that combines nationality prediction with general-purpose memory and Pipeworx utilities. Not too many or too few, though the mix of domains makes it feel slightly cluttered.
For the server's name 'nationalize', the nationality prediction tools are minimal (single and batch predict) with no additional country data or metadata. The inclusion of unrelated memory and Pipeworx tools dilutes focus and leaves obvious gaps in the primary domain.
Available Tools
10 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 of behavioral disclosure. It effectively describes the tool's behavior: it interprets natural language questions, selects appropriate data sources, and returns results. However, it doesn't mention limitations like rate limits, authentication needs, or potential errors, which would be helpful for a tool with no annotation coverage.
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 well-structured and front-loaded with the core functionality. Every sentence adds value: the first explains the purpose, the second describes the mechanism, the third provides usage guidance, and the examples illustrate practical applications. There's no wasted text, and it's appropriately sized for a single-parameter 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?
Given the tool's complexity (natural language processing with automatic tool selection) and no annotations or output schema, the description does a good job explaining what the tool does and how to use it. However, it doesn't describe the format or structure of returned answers, which would be important since there's no output schema. The examples help but don't fully compensate for this gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage, so the baseline is 3. The description adds value by explaining that the 'question' parameter should be in 'plain English' or 'natural language,' and provides concrete examples ('What is the US trade deficit with China?', etc.). This gives semantic context beyond the schema's generic 'Your question or request in natural language' description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool'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 mechanism ('Pipeworx picks the right tool, fills the arguments'). It distinguishes itself from siblings by emphasizing natural language interaction rather than requiring tool-specific knowledge.
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 if you want to browse or learn schemas). The examples further illustrate appropriate use cases, making it easy to distinguish from sibling tools like batch_predict or discover_tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
batch_predictAInspect
Predict nationalities for multiple first names at once (up to 10). Returns country codes with probability scores for each name. Use to process name lists efficiently.
| Name | Required | Description | Default |
|---|---|---|---|
| names | Yes | Array of first names to predict nationality for (maximum 10). |
Output Schema
| Name | Required | Description |
|---|---|---|
| results | Yes | Array of nationality predictions for each input 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 the full burden. It discloses key behavioral traits: the batch nature, request limit (10 names), and output format ('ranked nationality probabilities for each name'). However, it lacks details on error handling, rate limits, or authentication needs, leaving some gaps in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and efficiently conveys key details in two concise sentences. Every sentence earns its place by specifying the action, scope, limit, and output, with no wasted words 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 (batch prediction with a limit), no annotations, and no output schema, the description is largely complete. It covers purpose, usage, behavioral traits (batch, limit, output format), and parameters. However, it could improve by detailing error cases or response structure, slightly reducing completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the schema fully documenting the 'names' parameter (array of strings, max 10 items). The description adds minimal value beyond the schema, mentioning 'multiple first names' and 'up to 10 names', but does not provide additional syntax or format details. Baseline 3 is appropriate 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 clearly states the specific action ('Predict nationalities') and resource ('multiple first names'), distinguishing it from the sibling tool 'predict_nationality' by emphasizing batch processing ('multiple...in a single request') and the limit ('up to 10 names'). It avoids tautology by not merely repeating the tool name.
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: for processing 'multiple first names in a single request' with a 'maximum 10 names'. This directly contrasts with the sibling tool 'predict_nationality', which likely handles single names, providing clear alternative usage guidance without needing explicit exclusions.
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 provided, so description carries full burden. Discloses return format (paired data + pipeworx:// URIs), data sources (SEC EDGAR for companies, FDA for drugs), and value range constraints. No destructive behavior implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus type-specific breakdown. Every sentence provides unique information. 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?
Tool has no output schema, but description sufficiently explains return values (paired data, resource URIs). Covers entity types, data fields, and the benefit of batching. No missing critical info.
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 descriptions, but the description adds meaning: explains that 'values' depend on 'type' (tickers/CIKs for companies, drug names for drugs). This goes beyond the schema's enum and array definitions.
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?
States 'Compare 2–5 entities side by side in one call' with clear verb and resource. Differentiates from siblings by specifying entity types (company/drug) and data fields. No confusion with 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?
Explicitly explains when to use (comparing entities) and highlights efficiency ('Replaces 8–15 sequential agent calls'). Does not specify when not to use or name alternatives, but context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discover_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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool's function (searching and returning relevant tools) and some behavioral context (it should be called first in specific scenarios), but lacks details on rate limits, authentication needs, error handling, or what 'most relevant' means algorithmically. It provides basic operational context but misses deeper behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with two sentences that efficiently convey purpose and usage guidelines without any wasted words. Each sentence earns its place by providing critical information, 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?
Given the tool's moderate complexity (a search function with 2 parameters) and the absence of annotations and output schema, the description does a good job of covering key aspects like purpose and usage. However, it lacks details on output format (e.g., structure of returned tools) and error cases, which would be helpful for an AI agent. It's mostly complete but has minor 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?
The input schema has 100% description coverage, so the schema already documents both parameters (query and limit) thoroughly. The description does not add any additional meaning or semantics beyond what the schema provides, such as examples of effective queries or implications of the limit parameter. 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 tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and explicitly distinguishes it from siblings by specifying it should be called FIRST when there are 500+ tools available. It provides a clear action and scope that differentiates it from batch_predict and predict_nationality.
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: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context for when to use this tool versus alternatives, including a specific threshold (500+ tools) and a directive on priority.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetCInspect
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 full burden. It states 'Delete' implies a destructive mutation, but doesn't disclose if this is permanent, requires permissions, has side effects, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is a significant gap in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste, clearly front-loading the core action. Every word earns its place, 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?
Given this is a destructive mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, error handling, or return values, which are crucial for safe 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?
Schema description coverage is 100%, with the parameter 'key' fully documented in the schema as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format or examples, so it meets the baseline score 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 verb ('Delete') and resource ('a stored memory by key'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'recall' or 'remember', which likely interact with the same memory system, so it misses the top score.
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. Given siblings like 'recall' (likely to retrieve memories) and 'remember' (likely to store memories), the description offers no context for choosing 'forget' over other operations or prerequisites for deletion.
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 provided, the description carries full burden. It discloses the rate limit ('5 messages per identifier per day') and mentions the tool is free. This is good behavioral context for a feedback tool. However, it does not detail what happens after submission (e.g., if any confirmation is given), but the simplicity of the tool mitigates this.
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: three sentences that front-load the purpose, then provide usage guidelines and a behavioral note (rate limit). Every sentence adds necessary information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a straightforward feedback tool with no output schema, the description covers purpose, usage instructions, and constraints. It does not explain return behavior (e.g., if a success message is sent), but the context is sufficient for an AI agent to use the tool correctly. The presence of well-documented parameters compensates for the lack of 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?
The input schema has 100% coverage with clear descriptions for each parameter, including enum values for 'type'. The description adds marginal value by instructing to be specific and limiting message length, but the schema already explains the parameters well. Thus a baseline score of 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: 'Send feedback to the Pipeworx team.' It enumerates specific use cases (bug reports, feature requests, missing data, praise) which distinguishes it from sibling tools that perform other actions like prediction or data retrieval.
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 guidance on when to use this tool (for feedback) and gives instructions on what to include in the message: describe what you tried in terms of Pipeworx tools/data. It also explicitly states what not to include ('do not include the end-user's prompt verbatim'). It lacks explicit mention of when not to use it, but the context is sufficient 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.
predict_nationalityAInspect
Predict likely nationalities from a first name. Returns up to 5 country codes ranked by probability (0.0–1.0). Use when inferring someone's origin from their given name.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | First name to predict nationality for. |
Output Schema
| Name | Required | Description |
|---|---|---|
| name | Yes | The input first name |
| sample_size | Yes | Sample size from nationalize.io dataset |
| nationalities | Yes | List of predicted nationalities ranked by probability |
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: returns ranked results by probability, up to 5 country codes with scores. However, it doesn't mention accuracy limitations, data sources, rate limits, or error conditions that would be helpful for a prediction 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 perfectly concise: two sentences with zero wasted words. The first sentence states the purpose, the second specifies the output format. Every element 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?
For a single-parameter prediction tool with no annotations and no output schema, the description is adequate but has gaps. It explains what the tool does and the output format, but doesn't address confidence thresholds, limitations, or what the country codes represent (ISO codes?).
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 fully documents the single 'name' parameter. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 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 specific action ('predict'), resource ('nationalities for a given first name'), and scope ('most likely... ranked by probability'). It distinguishes from the sibling tool 'batch_predict' by specifying this is for a single name, not batch processing.
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 this tool: when you need nationality predictions for a single first name. It doesn't explicitly state when not to use it or mention the sibling 'batch_predict' as an alternative, but the single-name focus is implied.
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 full burden. It describes the dual behavior (retrieve by key vs list all) and mentions persistence across sessions, but doesn't disclose important behavioral traits like error handling (what happens if key doesn't exist), response format, or whether listing returns just keys or full memories. The description adds some context but leaves 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 that are front-loaded with core functionality. Every phrase earns its place: first sentence states the dual operations, second provides usage context. No redundant or unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description should do more. While it covers purpose and usage well, it doesn't describe the return format (what a 'memory' looks like when retrieved, what format the list returns), error conditions, or persistence details. For a memory retrieval tool with zero structured metadata, this leaves significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single parameter. The description adds valuable semantic context by explaining the conditional logic: 'omit key to list all keys' clarifies that the parameter is optional and its absence triggers a different behavior. This goes beyond what the schema provides about the parameter being optional.
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 sibling tools like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations.
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 ('retrieve context you saved earlier') and includes clear conditional logic ('omit key to list all keys'). It also distinguishes usage scenarios between retrieving specific memories versus listing all available ones.
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 the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool performs a write operation (implied by 'Store'), specifies persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), and hints at session scope. However, it does not mention potential limitations like storage size, rate limits, or 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?
The description is appropriately sized and front-loaded, with two sentences that efficiently convey purpose, usage, and behavioral details. Every sentence earns its place by adding distinct value: the first explains the core function and use cases, while the second clarifies persistence behavior without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (write operation with session memory), no annotations, and no output schema, the description is largely complete. It covers purpose, usage, and key behavioral aspects like persistence. However, it lacks details on return values (e.g., success confirmation) and potential errors, which would enhance completeness for a mutation tool.
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 fully documents both parameters ('key' and 'value'). The description adds minimal value beyond the schema by implying the parameters are used for storage but does not provide additional syntax, format details, or constraints. This 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 specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from siblings like 'forget' (delete) and 'recall' (retrieve). It provides concrete examples of what to store ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous.
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 offers 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 for persistence needs but lacks explicit exclusions or comparisons to sibling tools like 'recall'.
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 provided, so description carries full burden. Discloses version (v1), supported type (company), input formats, and return fields including resource URIs. Does not discuss error handling, auth, or rate limits, but covers core behavior adequately.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose, no filler. Efficiently conveys key information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given moderate complexity (2 params, no output schema), description sufficiently explains inputs, outputs, and benefit. Lacks mention of error states or limitations beyond version 1, but is largely complete for effective 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 coverage is 100% (both parameters described). Description adds value beyond schema by clarifying accepted input formats (ticker, CIK, name) with concrete examples like 'AAPL' and 'Apple', which aids agent in properly invoking the tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool resolves an entity to canonical IDs across Pipeworx data sources. Specifies it handles company type with ticker, CIK, or name, and returns ticker, CIK, name, and resource URIs. Distinguishes from sibling tools by emphasizing it replaces 2–3 lookup calls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Describes when to use (single call for entity resolution) and provides examples of accepted inputs. Lacks explicit when-not-to-use or alternative tools, but the sibling context shows no overlap in functionality.
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
Centralized credential management – store and rotate API keys and OAuth tokens in one place
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.
Discussions
No comments yet. Be the first to start the discussion!