postcodes
Server Details
Postcodes MCP — wraps postcodes.io UK postcode API (free, no auth)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-postcodes
- GitHub Stars
- 0
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.7/5 across 8 of 8 tools scored. Lowest: 2.9/5.
The postcode-related tools (lookup_postcode, nearest_postcodes, random_postcode, validate_postcode) have clear, distinct purposes with minimal overlap, but the memory tools (remember, recall, forget) and discover_tools create a mixed domain that could cause confusion. An agent might struggle to determine when to use memory tools versus postcode tools for related tasks.
The naming is mixed but readable: postcode tools use verb_noun patterns (e.g., lookup_postcode, validate_postcode), memory tools use simple verbs (remember, recall, forget), and discover_tools uses verb_noun. While not chaotic, the lack of a uniform convention across all tools reduces predictability.
With 8 tools, the count is reasonable for a server that combines postcode utilities with memory and discovery functions. It's slightly heavy for a pure postcode server but manageable, as each tool serves a distinct role without obvious bloat.
For the postcode domain, the surface covers key operations (lookup, validate, find nearest, random), but lacks advanced features like bulk processing or geographic calculations. The memory and discovery tools add unrelated functionality, creating a disjointed set that doesn't fully integrate into a cohesive workflow for any single domain.
Available Tools
9 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 'picks the right tool, fills the arguments, and returns the result' which provides useful context about the tool's orchestration behavior. However, it doesn't disclose important behavioral aspects like rate limits, authentication requirements, error handling, or what happens when no suitable data source is found.
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 front-loaded with the core functionality in the first sentence, followed by supporting details about how it works, and concludes with concrete examples. Every sentence earns its place by adding distinct value, with zero redundant information. The structure moves from general purpose to specific implementation to illustrative examples.
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 tool with 100% schema coverage but no output schema, the description provides excellent context about what the tool does and how to use it. The examples effectively illustrate the tool's capabilities. The main gap is the lack of information about return values or output format, which would be helpful since there's no output schema provided.
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 meaningful context by specifying that questions should be 'in plain English' and 'natural language', and provides three concrete examples that illustrate the expected format and scope of questions. This goes beyond what the schema provides about 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 verbs ('ask a question', 'get an answer') and resources ('best available data source'), distinguishing it from sibling tools like lookup_postcode or discover_tools. It explicitly explains that Pipeworx handles tool selection and argument filling, which is unique functionality not implied by the tool name alone.
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') and includes three concrete examples that illustrate appropriate use cases. It effectively communicates that this is the tool to use when you want natural language querying instead of manually selecting and configuring specific data tools.
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 effectively describes the tool's behavior: it's a search operation that returns relevant tools based on natural language queries, with implied read-only functionality (no destructive actions mentioned). However, it doesn't cover potential limitations like rate limits, authentication needs, 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 serve a clear purpose: the first explains what the tool does and returns, the second provides critical usage guidance. There's no wasted language and it's front-loaded with the core functionality.
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 natural language queries), no annotations, and no output schema, the description does well by explaining the purpose, usage context, and behavioral aspects. However, it doesn't describe the format of returned results or potential limitations, leaving some gaps for a tool discovery 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 schema already fully documents both parameters (query and limit). The description mentions 'describing what you need' which aligns with the query parameter, but adds no additional semantic context beyond what's in the schema. 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 the Pipeworx tool catalog') and resource ('tool catalog'), and distinguishes it from siblings by explaining it's for discovering tools rather than working with postcodes like the sibling tools. It explicitly mentions what it returns ('most relevant tools with names and descriptions').
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'), including a specific threshold (500+ tools) and context (finding tools for a task). It clearly distinguishes this from the sibling postcode tools by function.
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 the full burden of behavioral disclosure. It states this is a deletion operation, implying it's destructive, but doesn't clarify whether deletions are permanent, reversible, require specific permissions, or have 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, clear sentence with zero wasted words. It's front-loaded with the core action and resource, making it highly efficient and easy to parse.
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 operation with no annotations and no output schema, the description is inadequate. It doesn't explain what happens after deletion (e.g., success confirmation, error responses), nor does it address behavioral aspects like idempotency or permissions, leaving the agent with incomplete operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'key' parameter fully documented in the schema itself. The description adds no additional semantic context beyond what the schema already provides, such as examples of valid keys or deletion constraints, meeting 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 action ('Delete') and resource ('a stored memory by key'), making the purpose unambiguous. However, it doesn't distinguish this tool from potential siblings like 'recall' or 'remember' beyond the deletion aspect, which prevents a perfect 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 like 'recall' (which likely retrieves memories) or 'remember' (which likely stores them). It lacks any context about prerequisites, error conditions, or typical use cases for deletion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_postcodeBInspect
Get geographic details for a UK postcode (e.g., 'SW1A 1AA'). Returns coordinates, region, district, ward, and constituency.
| Name | Required | Description | Default |
|---|---|---|---|
| postcode | Yes | UK postcode to look up (e.g. "SW1A 1AA" or "SW1A1AA"). |
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 mentions 'Get' which implies a read operation, but does not disclose traits like rate limits, authentication needs, error handling, or what happens with invalid inputs. The description is minimal and lacks essential behavioral context beyond the basic purpose.
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 is front-loaded with the core purpose ('Get full geographic and administrative details'). There is no wasted language, and it directly communicates the tool's function without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool with behavioral implications. It does not explain what 'full geographic and administrative details' includes, potential response formats, or error cases. For a tool with no structured data beyond the input schema, more context is needed to guide 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?
The input schema has 100% description coverage, with the 'postcode' parameter well-documented in the schema (including examples). The description does not add any additional meaning or details about parameters beyond what the schema provides, so it meets 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 ('Get') and resource ('full geographic and administrative details for a UK postcode'), distinguishing it from siblings like 'nearest_postcodes' (which finds nearby postcodes) and 'validate_postcode' (which checks validity). It precisely communicates what the tool does without being vague or tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving detailed information about a specific UK postcode, but it does not explicitly state when to use this tool versus alternatives like 'nearest_postcodes' (for proximity searches) or 'validate_postcode' (for validation). No exclusions or clear context for choosing this tool over siblings are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
nearest_postcodesCInspect
Find nearby UK postcodes sorted by distance from a given postcode (e.g., 'SW1A 1AA'). Returns list with coordinates.
| Name | Required | Description | Default |
|---|---|---|---|
| postcode | Yes | UK postcode to find neighbours for (e.g. "SW1A 1AA"). |
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 'finds' nearest postcodes, implying a read-only operation, but doesn't cover aspects like rate limits, error handling, or output format. This leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's function without any unnecessary words. It is front-loaded and efficiently communicates the core purpose, 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 complexity (a geospatial query with no output schema) and lack of annotations, the description is incomplete. It doesn't explain what 'nearest' means (e.g., distance metrics, result limits) or the return format, which is critical for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'postcode' well-documented as a UK postcode with an example. The description adds no additional parameter details beyond what the schema provides, so it meets 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 tool's purpose with a specific verb ('Find') and resource ('nearest UK postcodes'), and it specifies the target ('to a given postcode'). However, it doesn't explicitly differentiate from sibling tools like 'lookup_postcode' or 'random_postcode', which likely have different functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where this tool is preferred, such as for proximity searches versus validation or random selection, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
random_postcodeAInspect
Get a random valid UK postcode with full geographic details. Returns coordinates, region, district, ward, and constituency.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 the tool's behavior by indicating it returns 'full geographic and administrative details,' but lacks information on potential limitations such as rate limits, data freshness, or error handling. The description is adequate but could be more detailed for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose and output details without any unnecessary words. It is front-loaded with the core action and resource, making it highly concise and effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is reasonably complete. It specifies what the tool does and the type of output expected. However, it could be more comprehensive by detailing the format of the 'geographic and administrative details' or any constraints, but for a low-complexity tool, it is largely sufficient.
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 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not add parameter details beyond the schema, but this is acceptable as there are no parameters. A baseline of 4 is appropriate since the schema fully covers the absence of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get a random valid UK postcode') and the resource ('with full geographic and administrative details'), distinguishing it from sibling tools like lookup_postcode, nearest_postcodes, and validate_postcode by emphasizing randomness rather than lookup, proximity, or validation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'random valid UK postcode,' suggesting it's for generating arbitrary postcodes rather than querying specific ones. However, it does not explicitly state when to use this tool versus alternatives like lookup_postcode for known postcodes or validate_postcode for verification, leaving some guidance implicit.
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 memories stored earlier in sessions, supports listing all memories, and handles both current and previous sessions. However, it lacks details on error handling, response format, or performance limits, leaving some gaps for a tool with no annotation support.
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 and concise, with two sentences that efficiently convey purpose and usage without redundancy. Every sentence adds value, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (1 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 since there's no output schema, leaving 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?
Schema description coverage is 100%, so the schema already documents the 'key' parameter. The description adds meaningful context: it explains that omitting the key lists all memories, while including it retrieves a specific memory by key, clarifying the parameter's role beyond the schema's basic 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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'), distinguishing it from siblings like 'remember' (store) and 'forget' (delete). It explicitly mentions retrieving context saved earlier in sessions, which clarifies its role in memory management.
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 guidance: use to retrieve context saved earlier, and specifies when to omit the key (to list all memories) versus include it (to retrieve a specific memory). It implicitly distinguishes from siblings by focusing on retrieval rather than storage or deletion, though it doesn't name alternatives directly.
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: it's a storage operation (implying mutation), specifies persistence differences based on authentication ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), and hints at session scope. However, it doesn't cover error cases, limits on key/value size, or concurrency behavior.
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: the first sentence states the core purpose, and subsequent sentences add valuable context without redundancy. Every sentence earns its place by explaining usage, authentication implications, and session duration—no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (storage with authentication-dependent persistence), no annotations, and no output schema, the description is mostly complete. It covers purpose, usage, and key behavioral aspects like persistence rules. However, it lacks details on return values (e.g., success confirmation) and potential error conditions, leaving 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, with clear descriptions for both 'key' and 'value' parameters. The description does not add any additional semantic meaning beyond what the schema provides (e.g., no examples of valid key patterns, no constraints on value content). Baseline score of 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (which likely retrieves) and 'forget' (which likely deletes). It explicitly mentions what can be stored ('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 provides clear context for 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 across sessions but lacks explicit exclusions or comparisons with 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.
validate_postcodeCInspect
Validate a UK postcode format (e.g., 'SW1A 1AA'). Returns whether it's valid and format details.
| Name | Required | Description | Default |
|---|---|---|---|
| postcode | Yes | UK postcode to validate (e.g. "SW1A 1AA"). |
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 checks validity but doesn't describe what 'valid' means (e.g., format, existence), potential error handling, rate limits, or response format. For a validation 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, clear sentence with no wasted words: 'Check whether a UK postcode is valid.' It is front-loaded and efficiently communicates the core purpose, making it easy for an agent to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, 100% schema coverage) but lack of annotations and output schema, the description is incomplete. It doesn't explain what constitutes validity, potential return values, or error cases. For a validation tool, this leaves critical behavioral aspects undocumented, reducing its helpfulness to an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'postcode' documented as 'UK postcode to validate (e.g., "SW1A 1AA").' The description adds no additional parameter semantics beyond this, as it doesn't elaborate on validation criteria or input constraints. Given the high schema coverage, 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: 'Check whether a UK postcode is valid.' It specifies the verb ('Check') and resource ('UK postcode'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'lookup_postcode' or 'nearest_postcodes,' which might also involve postcode validation as part of their functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where validation is preferred over lookup or other operations. This leaves the agent without explicit direction on tool selection, relying solely on the tool name and basic purpose.
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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