nouel
Server Details
Nobel MCP — wraps the Nobel Prize API v2 (free, no auth)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-nobel
- GitHub Stars
- 0
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Usage analytics
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Tool Definition Quality
Average 3.8/5 across 7 of 7 tools scored. Lowest: 2.9/5.
The tools have distinct primary purposes, but some overlap exists: ask_pipeworx and discover_tools both help find information, though ask_pipeworx is for direct queries while discover_tools is for tool discovery. The memory tools (remember, recall, forget) are clearly scoped, and the Nobel Prize tools (get_prizes_by_year, search_laureates) are specific, but the overall set mixes general-purpose and domain-specific tools, which could cause mild confusion.
Naming is mixed with no clear pattern: ask_pipeworx uses a verb_prefix format, discover_tools is verb_noun, forget is a single verb, get_prizes_by_year is verb_noun_preposition_noun, recall and remember are single verbs, and search_laureates is verb_noun. While readable, the conventions vary significantly, lacking a unified style across the toolset.
With 7 tools, the count is reasonable for a server that combines general querying, tool discovery, memory management, and Nobel Prize data. It's slightly broad in scope but manageable, avoiding bloat while covering multiple functionalities without being overwhelming for agents to navigate.
For the apparent domains, coverage is solid: ask_pipeworx and discover_tools handle general information retrieval, the memory tools provide full CRUD (create, read, delete) for session data, and the Nobel Prize tools offer search and filtering capabilities. Minor gaps might include updating memories or more advanced Nobel Prize queries, but core workflows are well-supported.
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 explains that Pipeworx automatically selects tools and fills arguments, which is valuable context. However, it doesn't mention limitations like response time, data source availability, error handling, or authentication requirements. The description doesn't contradict annotations (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 efficiently structured: it opens with the core functionality, explains the automation benefit, and provides three diverse examples. Every sentence adds value without redundancy. The length is appropriate for explaining this type of meta-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 (automated tool selection) and lack of annotations/output schema, the description does well to explain the high-level behavior and provide examples. However, it could better address potential limitations or edge cases. The absence of an output schema means the description doesn't need to explain return values, but more behavioral context would help.
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 schema already documents the single 'question' parameter. The description adds context by emphasizing 'plain English' and 'natural language,' and provides concrete examples that illustrate appropriate question formats. This enhances understanding beyond the basic schema 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 sibling tools by emphasizing natural language processing rather than structured queries.
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: 'No need to browse tools or learn schemas — just describe what you need.' It contrasts with alternatives by suggesting this is for users who want to avoid manual tool selection. The examples further clarify 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 carries full burden. It discloses return type (paired data + pipeworx:// resource URIs) and per-type metrics, but does not mention safety (read-only? destructive?), rate limits, or authentication requirements. The behavioral traits are partially clear 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?
Two sentences cover the core purpose and key detail. The first sentence is a strong verb-first statement of purpose. Minor improvement could be structuring the type-specific returns more clearly, but overall it's concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately covers return values (paired data, resource URIs). It also notes efficiency benefit (replaces 8–15 calls). However, it doesn't mention error conditions, validation rules, or max/min entity count beyond schema. For a tool with moderate complexity, it is fairly complete 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 coverage is 100%, with both parameters fully described. The description adds meaning beyond schema by detailing what each type returns (e.g., 'revenue, net income, cash, long-term debt' for company). This enriches the agent's understanding of the parameter values.
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 explicitly states 'Compare 2–5 entities side by side in one call' with specific metrics per type (company: revenue, net income, etc.; drug: adverse-event count, FDA approval count). This clearly distinguishes it from sibling tools like ask_pipeworx or resolve_entity, which are for different purposes.
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 the use case: comparing 2-5 entities in one call to replace 8-15 sequential calls. It implies efficiency benefits but doesn't explicitly state when not to use (e.g., for single entity queries) or alternatives.
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 search functionality and return format (tools with names and descriptions), but lacks details on error handling, rate limits, or performance characteristics. It adequately covers basic behavior but misses advanced operational 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 perfectly concise with two sentences that each serve a distinct purpose: the first explains the core functionality, the second provides critical usage guidance. Every word earns its place with zero redundancy or 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?
Given the tool's moderate complexity (search functionality with 2 parameters), no annotations, and no output schema, the description does well by explaining the purpose, usage context, and basic return format. However, it could benefit from mentioning what happens when no tools match or if there are authentication requirements.
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 mentions 'describing what you need' which aligns with the 'query' parameter, but adds no additional semantic context beyond what the schema provides. 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 tool's purpose with specific verbs ('search', 'returns') and resources ('Pipeworx tool catalog', 'most relevant tools with names and descriptions'). It distinguishes from siblings by focusing on tool discovery rather than prize or laureate searches, making the purpose explicit and differentiated.
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: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This clearly indicates the primary use case and context, offering strong direction for agent decision-making.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool deletes a memory, implying a destructive mutation, but doesn't address critical aspects like whether deletion is permanent/reversible, what permissions are required, error handling for non-existent keys, or side effects. 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 wasted words. It's front-loaded with the core action ('Delete') and resource ('stored memory'), making it immediately understandable. Every word earns its place in conveying the essential 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?
For a destructive mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address behavioral traits like permanence, permissions, or error handling, nor does it explain what happens after deletion (e.g., confirmation message, null return). Given the complexity of a delete operation, more context is needed for safe use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'key' fully documented in the schema as 'Memory key to delete'. The description adds no additional semantic context beyond restating this parameter's purpose, so it meets the baseline score of 3 where 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 action ('Delete') and resource ('a stored memory by key'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'recall' or 'remember' which likely handle memory retrieval/creation, leaving some ambiguity about the tool's unique role in the memory management system.
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. There's no mention of prerequisites (e.g., needing an existing memory key), exclusions, or relationships to sibling tools like 'recall' (likely for retrieval) or 'remember' (likely for creation). The agent must infer usage context 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_prizes_by_yearAInspect
Get all Nobel Prizes awarded in a specific year, optionally filtered by category (e.g., "Chemistry", "Peace"). Returns laureate names, categories, and citations.
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | Year to look up (e.g., 2023). Must be 1901 or later. | |
| category | No | Nobel Prize category: phy (Physics), che (Chemistry), med (Medicine), lit (Literature), pea (Peace), eco (Economics) |
Output Schema
| Name | Required | Description |
|---|---|---|
| year | Yes | The year queried |
| count | Yes | Number of prizes awarded that year |
| prizes | Yes | List of Nobel Prizes awarded in the year |
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 basic functionality but lacks details on permissions, rate limits, error handling, or response format. For a read operation with no annotation coverage, this is a significant gap.
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 core purpose and includes the optional parameter. There is zero wasted text, 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 (2 parameters, no output schema, no annotations), the description covers the basic purpose and parameters adequately but lacks behavioral details like response format or error conditions. It is minimally viable but has clear gaps in 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%, so the schema already documents both parameters ('year' and 'category') thoroughly. The description adds minimal value by mentioning the optional filtering by category but does not provide additional syntax or format details beyond what the schema 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 specific action ('Get all Nobel Prizes'), resource ('Nobel Prizes'), and scope ('awarded in a specific year, optionally filtered by category'). It distinguishes from the sibling tool 'search_laureates' by focusing on prizes rather than laureates.
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 ('awarded in a specific year, optionally filtered by category'), but does not explicitly state when not to use it or mention the sibling tool 'search_laureates' as an alternative for different queries.
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 carries full burden. It discloses the rate limit and a content rule, but does not explain what happens after submission (e.g., confirmation, storage), authentication needs, or any side effects. The information is adequate but not thorough.
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 with no fluff. The first sentence states purpose, the second adds usage guidelines and limitations. Front-loaded and compact.
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?
Output schema is absent, but for a feedback tool the return value is likely implicit. The description covers usage, constraints (rate limit, content rule), and purpose. It misses stating what the tool returns (e.g., success confirmation), but is otherwise sufficient for a simple feedback function.
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 does not add meaningful parameter-level information beyond what the schema provides. It reinforces the message parameter's intent but does not compensate for any gaps.
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 'Send feedback' and the resource 'Pipeworx team'. It enumerates specific use cases (bug reports, feature requests, missing data, praise) and adds a guideline about what not to include. This distinguishes it from siblings which are data retrieval or processing 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 when to use the tool: for bug reports, feature requests, missing data, or praise. It also provides a behavioral constraint ('do not include the end-user's prompt verbatim') and a rate limit. However, it does not contrast with sibling tools or state when to avoid this tool.
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?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it retrieves or lists memories, works across sessions, and requires a key for retrieval (omit for listing). However, it lacks details on error handling (e.g., if key doesn't exist), return format, or performance limits. The description doesn't contradict annotations (none provided).
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 in the first sentence, followed by a concise usage guideline. Every sentence earns its place by providing essential information without redundancy, making it efficient 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 low complexity (1 optional parameter, no output schema, no annotations), the description is mostly complete: it covers purpose, usage, and parameter semantics. However, it lacks details on return values or error cases, which would be helpful for an agent. Since no output schema exists, some gap remains, but it's adequate for this simple 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 baseline is 3. The description adds meaningful context beyond the schema: it explains that omitting the key lists all memories, and ties the parameter to retrieving context from earlier sessions. This enhances understanding of the parameter's role, justifying a score above baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'), and distinguishes it from siblings like 'remember' (store) and 'forget' (delete). It explicitly mentions retrieving context saved earlier in sessions.
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 a previously stored memory by key, or list all stored memories (omit key).' It also specifies context: 'Use this to retrieve context you saved earlier in the session or in previous sessions,' clearly indicating its role versus alternatives like 'remember' for storage.
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 and does well by disclosing key behavioral traits: it explains persistence differences (authenticated vs. anonymous sessions with 24-hour limit) and the tool's purpose for cross-call context. However, it doesn't mention potential limitations like storage capacity 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 perfectly concise with two sentences that each earn their place: the first states the core purpose, and the second adds crucial behavioral context about persistence. No wasted words, and information is front-loaded appropriately.
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 2-parameter tool with no annotations and no output schema, the description provides good context about what the tool does and its persistence behavior. However, it doesn't explain what happens on success/failure or return values, leaving some gaps in 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%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter semantics.
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 verb ('Store') and resource ('key-value pair in your session memory'), and distinguishes it from siblings by specifying its unique storage function compared to tools like 'recall' (likely retrieval) and 'forget' (likely deletion).
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 ('save intermediate findings, user preferences, or context across tool calls'), but doesn't explicitly mention when not to use it or name specific alternatives among sibling tools 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_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 disclose behavioral traits. It states the function (resolve to canonical IDs) and expected outputs (ticker, CIK, name, URIs), but does not mention idempotency, error handling, required permissions, or whether it modifies data. The version note ('v1') is mentioned but not explained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, fitting in a single sentence with additional details about the version and examples. It is front-loaded with the primary purpose. However, the version information could be structured more clearly, and the list of outputs is inline rather than bulleted.
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 moderate complexity (2 parameters, no output schema), the description covers the core functionality well: what it does, inputs, outputs, and that it replaces multiple calls. However, it lacks details on what happens when an entity is not found, the format of resource URIs, and any limitations or edge cases. Since there is no output schema, more information about the return structure would improve 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% for both parameters. The description adds examples of valid values (e.g., 'AAPL', '0000320193', 'Apple') and clarifies that only 'company' is supported in v1, which adds meaning beyond the schema's enum and description. This helps the agent understand parameter usage better than the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool resolves an entity to canonical IDs across Pipeworx data sources in a single call. It specifies the supported entity type ('company') and the input formats (ticker, CIK, name). It distinguishes itself from sibling tools like search_laureates by focusing on entity resolution and explicitly mentions it replaces multiple lookup calls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used for entity resolution when you have a ticker, CIK, or company name, and mentions it replaces 2-3 lookup calls, suggesting it is more efficient. However, it does not explicitly state when not to use it or provide comparisons to sibling tools like ask_pipeworx that might also perform lookups.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_laureatesCInspect
Search Nobel Prize laureates by name and/or category (e.g., "Physics", "Medicine", "Literature"). Returns biography, prizes won, and award motivation.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Full or partial name of the laureate (e.g., "Einstein", "Marie Curie") | |
| category | No | Nobel Prize category: phy (Physics), che (Chemistry), med (Medicine), lit (Literature), pea (Peace), eco (Economics) |
Output Schema
| Name | Required | Description |
|---|---|---|
| count | Yes | Total number of laureates matching the search |
| laureates | Yes | List of Nobel Prize laureates |
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 the tool returns 'biography, prizes won, and motivation,' which adds some context about output content. However, it lacks details on critical behaviors like pagination, rate limits, error handling, or whether searches are case-sensitive/fuzzy. For a search tool 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 that front-loads the core functionality ('Search Nobel Prize laureates by name and/or prize category') and follows with output details. There's no wasted verbiage, but it could be slightly more structured (e.g., separating usage from output).
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 (search with two optional parameters), no annotations, and no output schema, the description is minimally adequate. It covers what the tool does and what it returns, but lacks context on behavioral traits, error cases, or sibling tool differentiation. Without an output schema, the description's mention of return content ('biography, prizes won, and motivation') is helpful but not fully compensatory.
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 input schema already fully documents both parameters (name and category) with descriptions and examples. The description adds no additional parameter semantics beyond what's in the schema—it merely restates the parameters without providing extra context like format nuances or interaction effects. 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 tool's purpose: 'Search Nobel Prize laureates by name and/or prize category.' It specifies the verb ('Search') and resource ('Nobel Prize laureates'), making the function unambiguous. However, it doesn't explicitly differentiate from the sibling tool 'get_prizes_by_year' (which appears to search by year rather than name/category), so it doesn't reach the highest 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?
The description provides no guidance on when to use this tool versus alternatives. It mentions the parameters (name and category) but doesn't explain scenarios where this search is preferred over the sibling 'get_prizes_by_year' or other potential methods. There's no mention of prerequisites, limitations, or typical use cases.
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
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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.
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