Data File Analysis MCP Server
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation3/5
The two tools are clearly distinct by file type (Parquet vs CSV), so an agent should be able to tell them apart when the file format is known. However, they share the identical purpose and structure (summarizing file dimensions), and there is no unified or generic tool to handle either format, which could cause confusion if an agent must guess the format.
Naming Consistency4/5Both tools follow the same verb_noun pattern (summarize_parquet_file, summarize_csv_file), sharing the identical 'summarize' verb and 'file' suffix. The only variation is the file format in the middle, which is consistent and predictable, though the format should ideally be a parameter rather than baked into the tool name.
Tool Count3/5With only 2 tools, this sits at the boundary of 'too few.' It's not a single trivial tool, but two near-identical tools covering only file summarization feels thin for a 'Data File Analysis' server, which implies a much broader analysis scope than just counting rows and columns.
Completeness2/5For a server named 'Data File Analysis,' this surface is severely limited: there is no way to read, sample, filter, compute statistics, inspect columns/types, or join files. Even for a narrow summarization-only scope, neither tool supports inspecting column schemas or data previews, leaving significant gaps that will cause agent failures for common analysis tasks.
Average 3.5/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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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 are provided, so the description carries the full burden of behavioral disclosure. The description mentions the return value (a string describing dimensions) but does not disclose the actual output format, whether it reads the entire file into memory, error behavior for missing files, or whether it handles nested/partitioned Parquet files. For a read-only tool, the absence of annotations and lack of behavioral detail is a gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and uses the Args/Returns structure for clarity. It's front-loaded with the core purpose in the first sentence. Every sentence earns its place, though the Args/Returns formatting is slightly verbose for a single-parameter tool.
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 1-parameter, read-only tool with an output schema present, the description is reasonably complete. It states inputs and return type. However, the return format is vague ('a string describing the file's dimensions') and the /data directory constraint could use more precision (absolute vs relative paths, access permissions). Given the low complexity, this is adequate but not thorough.
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?
With a single parameter and 0% schema description coverage, the description compensates somewhat by specifying the filename format with an example ('sample.parquet') and the /data directory location. However, it doesn't clarify whether the path extension matters, whether it accepts paths with subdirectories under /data, or what happens with invalid filenames. The description adds moderate value beyond the bare schema.
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 description clearly states the verb+resource: 'Summarize a Parquet file by reporting its number of rows and columns.' This is specific about what it does and distinguishes it from the sibling summarize_csv_file by explicitly naming the Parquet format. However, it doesn't explicitly contrast itself with the sibling tool, so it doesn't fully maximize sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating the tool works on files in the /data directory, but gives no explicit guidance on when to choose this over summarize_csv_file or any exclusions. The file-format naming (Parquet vs CSV) makes the usage distinction implicit but not explicitly 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?
No annotations are provided, so the description carries the burden. It describes the operation (reporting rows and columns) which is a read-only analysis, but doesn't explicitly state it's non-destructive or disclose edge cases like malformed CSVs, empty files, or large files. The return value is described as 'a string describing the file's dimensions,' which is helpful, but behavioral detail is thin.
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: a single summary sentence plus minimal Args/Returns documentation. Every sentence earns its place, with zero waste or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple single-param read-only tool with an output schema. The description explains purpose, parameter format, and return type. For the tool's low complexity, it is reasonably complete. Minor gaps: no handling of error cases or explicit confirmation of non-destructive read behavior, but these are minor for a summary tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate for the single parameter. It does: 'filename: Name of the CSV file in the /data directory (e.g., 'sample.csv')' adds the directory constraint and a concrete example format, which goes beyond the bare schema field. Baseline for 1 param with 0% coverage would be 4, and this delivers exactly that.
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?
Description states a specific verb+resource: 'Summarize a CSV file by reporting its number of rows and columns.' It clearly distinguishes from sibling summarize_parquet_file (CSV vs parquet). Could be improved but purpose is clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It states the file must be in the /data directory, providing some context, and the tool is clearly for CSV vs parquet. However, it doesn't explicitly note when to use this vs summarize_parquet_file or any exclusions, relying on the format in the name.
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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