FrankSpace Public MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, closed-world, idempotent behavior. The description adds that it searches live UK listings, which is useful context beyond annotations. No contradiction. Could mention result limit or ordering but overall adequate.

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 concise sentences with no wasted words. Front-loads the core purpose and adds relevant details in the second sentence.

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?

No output schema exists, so the description should cover return structure. It only says 'Returns matching live UK listings' without details on fields, scoring, pagination, or result count. For an AI search tool, this is insufficient.

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%, so baseline is 3. The description adds an example query and lists handled criteria (budgets, headcount, etc.), providing meaningful semantics beyond the schema's brief 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 searches FrankSpace workspaces using plain-English queries, listing supported criteria (budgets, headcount, etc.) and notes it returns live UK listings. It distinguishes from siblings by emphasizing natural language vs structured search.

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 use for natural language queries (the same AI parser as the app) but does not explicitly exclude structured search or individual workspace lookup. It provides clear context for when to use but lacks exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark it as read-only, idempotent, and closed-world. The description adds valuable context about the content of the skill (e.g., data format, limitations), which helps the agent understand what to expect without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences that front-load the action ('Fetch the FrankSpace Agent Skill') and succinctly cover the document's content and recommended usage. Every sentence adds value.

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?

The tool is simple (one optional param, no output schema). The description explains the skill's content sufficiently for an agent to understand when and why to call it. Minor omission: no mention of the parameter's effect, but the tool works without it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% description coverage for the single optional parameter 'client_hint,' so baseline is 3. The description does not elaborate on this parameter, but it is not necessary for understanding the tool's core purpose.

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 it fetches the 'FrankSpace Agent Skill' document, which instructs on querying inventory, reading data (prices as pence, IDs as UUIDs), and understanding server limits. This clearly differentiates it from sibling tools like 'ai_search' or 'get_workspace.'

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 advises reading the skill 'once before your first search if you are unfamiliar with FrankSpace,' providing a clear use case. It does not explicitly state when not to use it or mention alternatives, but the context is sufficiently clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, idempotentHint, and openWorldHint. The description adds that details are 'public', which is helpful but minimal. It does not contradict annotations, but adds little beyond them.

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 immediately conveys the core purpose. No unnecessary words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read tool with strong annotations and a self-explanatory schema, the description sufficiently explains what the tool does and what it returns ('full public details'). No output schema is needed.

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 for both parameters. The description does not add any additional meaning beyond the schema's own parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Fetch'), a clearly defined resource ('full public details for a single FrankSpace workspace'), and a precise method ('by its ID (UUID)'). This effectively distinguishes it from sibling search tools like 'search_workspaces'.

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 implicitly indicates usage (fetching a single workspace by ID), but lacks explicit guidance on when to use this tool versus alternatives like 'search_workspaces' or 'ai_search'. No 'when not to use' or direct comparison is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and idempotentHint. The description adds context about the domain (live UK workspace listings) and filtering capabilities, but does not detail return format or pagination behavior, which is acceptable given annotations.

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 with no fluff. Purpose is front-loaded, and the alternative tool reference is efficiently placed. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the main filtering options and domain. However, it omits mention of the 'limit' parameter (pagination) and the 'client_hint' parameter, leaving minor gaps. For a search tool without output schema, this is acceptable but not fully complete.

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 80%, so baseline is 3. The description adds value by specifying that location can be city, postcode, or submarket, and mentions size band and max price. This clarifies the intended use beyond the schema's individual parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Search' and the resource 'live UK workspace listings on FrankSpace'. It distinguishes from sibling 'ai_search' by specifying structured filter capabilities and explicitly directing richer queries to 'ai_search'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly instructs when to use this tool: for filtering by location, size band, and max price. Provides an alternative ('ai_search') for natural-language queries, making the choice clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds behavioral context beyond annotations: feedback is reviewed by the team, nobody replies to this channel, and it specifies what kind of feedback is appropriate. Annotations are neutral (no readOnly, no destructive hints), and the description does not contradict them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear examples and exclusions. It is front-loaded with purpose and then details. Though slightly long, every sentence adds value.

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 complexity (5 parameters, 1 required) and no output schema, the description provides sufficient context for agents to understand the tool's purpose and how to report issues. It covers the types of problems and what to include.

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 all five parameters thoroughly. The tool description does not add additional parameter-level detail beyond the schema. Baseline 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 uses a specific verb ('Report') and explicitly lists the types of problems (data errors, missing data, docs unclear, tool problems) that fall under this tool. It clearly distinguishes from sibling tools like ai_search, get_workspace, etc., which serve 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 provides explicit when-to-use ('Report something wrong or missing') and when-not-to-use ('Do NOT use this to contact a property owner...', 'Enquiries and viewings go through the listing page'). It also gives guidance on sending one clear report rather than several.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.