Fotocasa1 MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a distinct purpose: location autocomplete, property listing, and property details. No overlap in functionality.
Naming Consistency4/5Names use consistent lowercase_with_underscores pattern. Two follow verb_noun (get_suggestions, list_properties) and one is noun_noun (property_details), which slightly deviates but remains clear.
Tool Count3/53 tools is minimal but reasonable for a read-only property browsing server. Could be expanded with search or filtering but not critically thin.
Completeness3/5Covers the basic workflow of location search, property listing, and details. However, missing operations like filtering by criteria other than location or user interaction features reduces completeness.
Average 3.3/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description gives no behavioral traits such as read-only nature, side effects, or error conditions. It's insufficient for an agent to understand implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise with two sentences that convey the core purpose and requirement. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description is adequate but lacks details on return value or constraints. It covers the minimum.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the parameter description in the schema already explains the format. The tool description adds minimal value by restating the requirement for the property ID.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The verb 'Get' and resource 'property details' clearly indicate the tool retrieves details for a property. While it distinguishes from siblings like list_properties, it doesn't explicitly differentiate usage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. It only states the required parameter, not the context or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description does not disclose behavioral traits beyond the basic function. It fails to mention whether the operation is read-only, requires authentication, has rate limits, or any side effects. The minimal phrasing offers no additional insight into 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that communicates the essential purpose without any extraneous words. Every word earns its place, making it highly efficient and easy to process.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (one parameter, no output schema), the description is minimally adequate. However, it omits information about the output structure (e.g., whether results are strings or objects), which would be helpful for the agent to understand the return format. Slight improvement needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although the input schema has 100% description coverage, the parameter description only provides an example value ('madrid'). The tool description adds context that the query is for location suggestions, which gives semantic meaning beyond the schema, but it does not elaborate on the expected format or constraints, resulting in adequate but not exceptional value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does 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 'get' and resource 'location suggestions', explicitly indicating autocomplete functionality. It is distinct from sibling tools list_properties and property_details, which focus on properties rather than location suggestions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 does not mention scenarios, prerequisites, or conditions under which this tool should be preferred, leaving the agent without explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It does not mention that the tool is read-only, what the response contains (e.g., list of properties, count, pagination), or any side effects. It also fails to indicate that the tool requires multiple mandatory parameters or how errors are handled, leaving 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the purpose in the first sentence and adding prerequisite and parameter guidance in the second. Every word is functional; no wasted space.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 25 parameters (7 required) and no output schema, this description is too sparse. It omits crucial context: how to combine parameters for filtering, that operation is required, and what the output looks like. The agent would need to infer or guess many usage details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 value by explaining that locationId, lat, lon come from the autocomplete endpoint and that propertyType can be set to specific values like Homes, Premises, etc. However, it does not elaborate on many other parameters (e.g., minPrice, operation), relying on weak schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List Properties' as the action, identifies required parameters (locationId, lat, lon) and their source, and explains the propertyType parameter's role in filtering by property type. This differentiates it from sibling tools like get_suggestions (location autocomplete) and property_details (single property info).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description instructs the agent to obtain locationId, lat, and lon from the autocomplete endpoint, providing a clear prerequisite. It also advises picking the propertyType for extraction. However, it lacks explicit guidance on when to use this tool vs. alternatives (e.g., when to call property_details instead) or situations to avoid.
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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