geoanalisis-mcp

by alexrobl

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.2.9
Disambiguation5/5
Each tool has a distinct purpose: schema info, layer listing, interactive rendering, statistics, data reading, geometry preview, static image export, and formal cartographic export. The two export tools are clearly differentiated by use case (quick exploration vs. formal product).
Naming Consistency5/5
All tools follow a consistent snake_case verb_noun pattern (e.g., get_layer_schema, list_layers, render_map, export_map_image). No naming style deviations.
Tool Count5/5
With 8 tools, the server is well-scoped for GIS data inspection, visualization, and export. The count covers essential operations without being overwhelming or insufficient.
Completeness4/5
The tool set covers key read-only GIS operations: layer listing, schema inspection, data reading, statistics, preview, and two types of map exports. Missing spatial analysis operations (e.g., buffer, intersect) but that appears out of scope. Slight gap but overall complete for its purpose.
Average 4.2/5 across 8 of 8 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 25 commits in the last 12 weeks
- Last stable release on July 22, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description provides some behavioral detail (list of returned metadata) but does not disclose side effects, safety, or performance. It implies a read operation but does not state readOnly or destructive flags.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two clear paragraphs: one for purpose and return values, one for parameters. No wasted sentences, though a structured list for returns might enhance readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple metadata tool with 2 params and an output schema present, the description covers the main purpose, return fields, and parameter explanations. It is largely complete but could mention read-only behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given 0% schema coverage, the description explains both parameters: path (path to spatial file/directory) and layer (name, default first available). This adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it gets the complete schema of a spatial layer and lists return fields. However, it does not explicitly differentiate from sibling tools like list_layers or read_features, which would help an agent select correctly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does 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 (e.g., list_layers for layer listing, read_features for data). The usage context is implied but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. It discloses supported formats and return values, and notes that .gdb is a directory. However, it does not mention edge cases, error handling, or permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is very concise: 5 lines with front-loaded purpose. No redundant information; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given an output schema exists, description covers key return fields and supported formats. For a listing tool, it is mostly complete, though it could mention error scenarios or file permission requirements.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage for the only parameter 'path'. Description adds meaning by specifying 'absolute path' and clarifying .gdb is a directory, helping agents beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool lists available layers in a spatial data source, specifying supported formats and return fields. However, it does not explicitly differentiate from sibling tools like get_layer_schema or read_features.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives. Usage is implied by the verb 'list layers' but lacks when-not or alternative tool mentions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description provides basic behavioral information (numeric vs text stats, max_features limit) but lacks disclosure of side effects, performance, or error conditions; since no annotations exist, more transparency would be beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear summary, followed by detailed stats and parameter documentation, all in a concise format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the purpose, parameters, and basic behavior; with an output schema available, it provides sufficient contextual completeness, though minor aspects like error handling are missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description provides parameter explanations (path, layer, fields, max_features) that add meaning beyond the raw schema, though concise.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool computes descriptive statistics per field in a spatial layer, distinguishing between numeric and text fields, and is distinct from sibling tools like get_layer_schema or read_features.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for analyzing field statistics but lacks explicit guidance on when to use this vs. other tools or any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and discloses many behaviors: inline vs disk DPI differences, basemap fallback, CRS handling, style precedence, and warnings about extra layers. It does not explicitly state it is read-only or describe file creation side effects, but given the tool's nature, this is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose but well-structured with a clear high-level overview followed by an 'Args:' section. Each parameter gets a focused explanation. Some lines (e.g., basemap) could be slightly more concise, but overall it is front-loaded and organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 19 parameters, no annotations, and an existing output schema, the description covers all parameters with appropriate detail. It mentions the return of a textual report, and the output schema presumably documents the return format, so the description is complete for agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description documents every parameter with type, default, examples, and nuanced details (e.g., style format, basemap URLs, extra layer attributes). This fully compensates for the missing schema descriptions, adding significant value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a CartoDB Positron basemap image inline and saves a high-res copy to disk, plus returns a text report of applied symbology. This distinguishes it from siblings like 'render_map' (likely just inline) and 'export_map_cartographic' (cartographic export) by emphasizing both inline display and disk saving with symbolic report.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus its siblings (e.g., render_map, export_map_cartographic). It does not mention when not to use or suggest alternatives, leaving the agent to infer from parameter details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description discloses that it returns a GeoJSON FeatureCollection, supports filters (where, bbox), defaults limit to 10, and provides error handling when output size is exceeded. It is read-only in nature and non-destructive, which is inferred but not stated explicitly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: a single sentence for purpose, followed by a parameter list with brief explanations, and a final sentence on error behavior. No unnecessary words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists and the description covers return type (GeoJSON FeatureCollection), all parameters, and error handling, the description is complete for the tool's complexity. No additional information is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description explains each parameter comprehensively: path, layer, limit, where (with SQL example), and bbox (with spatial filter format). This adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it reads features from a layer and returns them as GeoJSON FeatureCollection. The verb 'Lee' (reads) and resource 'capa' (layer) are specific, and it is clearly distinct from siblings like get_layer_schema or list_layers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs. alternatives (e.g., get_layer_schema for schema, render_map for visualization). The description implies usage for reading feature data but does not mention exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses return content (index, geometry type, WKT, bounding box) and the limit-driven scope, leaving no ambiguity about the tool's read-only, non-destructive behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus a structured Args section with no superfluous text, front-loaded with the core purpose, achieving maximum clarity with minimal length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers return format and parameter semantics adequately for a preview tool, but lacks mention of error handling (e.g., invalid path) or performance characteristics, which would be nice for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description thoroughly explains each parameter's role (path, layer with default null meaning first, limit with default 5), adding meaning beyond the schema's type and default values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies a clear verb ('preview') and resource ('geometries of first N features as WKT'), which distinguishes it from sibling tools like read_features (full feature data) and list_layers (layer enumeration).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for quick geometric previews but does not explicitly state when to prefer this over alternatives like read_features or render_map, nor does it provide exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses key behaviors: map layout components, return of a textual symbology report, DPI effect on basemap tile loading, extent adjustment with 'where' filter, and default marker size calculation. Lacks explicit safety (read-only) statement but otherwise thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: overview, usage guidance, then detailed parameter list. It front-loads the essential purpose. However, it is lengthy and includes some redundancy (e.g., repeated 'Ver export_map_image'), making it slightly less concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 17 parameters, no schema descriptions, and no annotations, the description covers all parameters and describes the output. It lacks explicit error handling or output format details, but overall is fairly complete for a complex tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description fully compensates by explaining each parameter in the 'Args' block, including purpose, defaults, examples, and important notes (e.g., 'extra_layers' requiring explicit colors). This adds significant meaning beyond the plain schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a technical cartographic map with a formal layout, listing its components. It explicitly distinguishes itself from sibling tool 'export_map_image' by stating 'Para exploración rápida usar export_map_image', making the purpose and differentiation evident.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: 'Usar para productos formales' and a direct alternative: 'Para exploración rápida usar export_map_image'. It also includes a behavioral warning to verify the textual report before claiming style changes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It details interactive features (pan, zoom, click inspection, layer control, legend, scale bar), sandbox limitation for basemap, and output format (high-fidelity HTML). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Long but well-structured: starts with bold purpose, then usage guidelines, output description, then detailed parameter list. Every sentence adds value. Could be slightly more concise, but justified by complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers behavior, output interactivity, sandbox limitation, and parameter details. Despite 13 parameters, the description is complete and leaves no major gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description thoroughly explains all 13 parameters with formats, defaults, and usage tips (e.g., extra_layers requires distinct colors). Adds significant value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is the 'PRIMARY tool for visualizing spatial data' and specifies it should be used whenever the user asks for a map, visualization, render, or view of spatial layers. It contrasts with siblings like export_map_image by emphasizing interactive map output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use ('SIEMPRE que el usuario pida un mapa') and when not to use ('No uses capacidades internas de visualización de Claude... NUNCA es necesario'). Provides clear alternatives and usage context.
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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