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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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
7 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.
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) |
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.
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.
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) |
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.
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