Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@csv-explorer-mcp sample 10 random rows from customers.csv"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server for exploring and analyzing CSV files. Provides tools for inspection, sampling, schema inference, statistics, filtering, and more.
npm install
npm run buildRelated MCP server: mcp-csv-analyst
Add to your MCP configuration:
{
"mcpServers": {
"csv-explorer": {
"command": "node",
"args": ["path/to/dist/index.js"]
}
}
}Get an overview of a CSV file including size, row/column count, detected delimiter, and a preview of the data. Large field values are automatically truncated with content-type hints.
csv_inspect({ file: "/path/to/data.csv", previewRows: 5 })Get sample records using various sampling strategies.
csv_sample({ file: "/path/to/data.csv", mode: "random", count: 10 })
// modes: "first", "last", "random", "range"Infer the schema by sampling records. Returns column names, types, and nullability.
csv_schema({ file: "/path/to/data.csv", sampleSize: 1000 })
// outputFormat: "inferred", "json-schema", "formatted"Collect aggregate statistics for fields. Includes min/max, mean, median, stdDev for numeric fields, and top values for categorical fields.
csv_stats({ file: "/path/to/data.csv", fields: ["price", "category"] })Search for records where a field matches a regex pattern.
csv_search({ file: "/path/to/data.csv", field: "email", pattern: "@example\\.com$" })Filter records using query expressions. Supports comparisons (==, !=, <, >, <=, >=), text operations (contains, startswith, endswith, matches), and compound queries (AND, OR).
csv_filter({ file: "/path/to/data.csv", query: 'status == "active" AND age > 30' })Validate a CSV file for syntax errors and optionally against a schema.
csv_validate({
file: "/path/to/data.csv",
schema: {
columns: [
{ name: "id", type: "integer", required: true },
{ name: "email", type: "string", pattern: "^[^@]+@[^@]+$" }
]
}
})Read new records appended since a cursor position. Use for monitoring actively-written files.
csv_tail({ file: "/path/to/data.csv", cursor: 1024, maxRecords: 100 })Get the current end-of-file position for use with csv_tail.
csv_get_cursor({ file: "/path/to/data.csv" })Compare two CSV files and report differences.
csv_diff({ file1: "/path/to/old.csv", file2: "/path/to/new.csv", keyField: "id" })Extract a specific field value from a CSV record. Use for retrieving large/truncated field data. Can write to file for binary data (e.g., base64 images).
// Get field value inline
csv_extract({ file: "/path/to/data.csv", field: "description", line: 5 })
// Decode base64 and write to file
csv_extract({
file: "/path/to/data.csv",
field: "screenshot",
line: 1,
decode: "base64",
outputFile: "/tmp/screenshot.png"
})List fields containing large values (e.g., base64 images, JSON blobs). Helps identify which fields were truncated in csv_inspect.
csv_large_fields({ file: "/path/to/data.csv", threshold: 1000, sampleRows: 100 })Streaming Architecture: Memory-efficient processing of large files
Auto-Detection: Automatically detects delimiters (comma, tab, semicolon, pipe) and encoding
Smart Truncation: Large field values are truncated with content-type hints (base64, JSON, HTML)
Query Engine: Filter records with SQL-like expressions supporting AND/OR logic
Schema Inference: Detect column types (string, integer, number, boolean, date, email, url)
Online Statistics: Uses Welford's algorithm for efficient single-pass statistics
# Run tests
npm test
# Build
npm run build
# Watch mode
npm run devMIT
Compare two CSV files and report differences.
| Name | Required | Description | Default |
|---|---|---|---|
| file1 | Yes | Absolute path to the first CSV file | |
| file2 | Yes | Absolute path to the second CSV file | |
| keyField | No | Field to use as unique key for matching records | |
| maxDiffs | No | Maximum differences to report (default: 100) | |
| compareFields | No | Specific fields to compare (default: all) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'report differences' but does not explain whether this is a read-only operation, what output format is returned, how differences are matched (e.g., using keyField), or any edge cases like identical files. This is a significant omission for a tool that could be confused with a mutating operation.
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, grammatically correct sentence with no filler. It is appropriately concise and front-loaded, stating the primary action and result. However, it could be expanded moderately without losing conciseness to cover key behavioral details, so it is just short of perfect.
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?
Despite having 5 parameters and no output schema or annotations, the description provides only a high-level summary. It fails to explain return values, the meaning of 'differences' (row-level, column-level, or both), the role of keyField, or the impact of maxDiffs and compareFields. For a tool with this complexity, the description is far from 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?
The input schema has 100% description coverage for all 5 parameters, so the schema already documents what each parameter does. The description adds no additional meaning beyond the schema, and does not clarify parameter interactions (e.g., how keyField and compareFields work together). Baseline 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 a specific action ('Compare two CSV files') and the result ('report differences'). This distinguishes it from sibling tools like csv_inspect or csv_stats, which serve different purposes. However, it does not fully specify the scope of differences (e.g., row-level or column-level), which is a minor gap.
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?
No guidance is provided on when to use this tool versus alternatives such as csv_search or csv_filter. The phrase 'Compare two CSV files' implies the primary use case, but there is no mention of prerequisites, typical scenarios, or exclusions. The description does not help the agent decide between this and sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract a specific field value from a CSV record. Use for retrieving large/truncated field data. Can write to file for binary data (e.g., base64 images).
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Absolute path to the CSV file | |
| line | No | Line number (1-indexed data row, default: 1) | |
| field | Yes | Field name to extract | |
| decode | No | Decode base64 data before output (default: none) | |
| outputFile | No | Write extracted data to this file path instead of returning |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It discloses the ability to write to file for binary data (e.g., base64 images) and implies it handles large/truncated fields. However, it does not mention potential side effects, output truncation behavior, or the decode parameter, leaving some gaps.
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 three concise sentences with no redundant information. It front-loads the core purpose and adds only high-value behavioral context, making every sentence earn 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?
Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description covers the essential purpose, key use case, and a notable behavioral trait (file writing). It does not explain all parameters or return behavior, but the schema compensates for parameter details, making the description reasonably 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 coverage is 100%, so the baseline is 3. The description adds some meaning by indicating that outputFile is for binary data and that the tool handles large/truncated field data, but it does not significantly elaborate on parameter usage beyond what the schema already provides.
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 ('Extract a specific field value from a CSV record') with a specific verb and resource. It also distinguishes itself from siblings by specifying the use case for large/truncated field data, which is unique among the related tools.
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: 'Use for retrieving large/truncated field data.' It also mentions writing to file for binary data, giving additional usage guidance. However, it does not explicitly name alternative tools or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Filter records using query expressions. Supports comparisons (==, !=, <, >, <=, >=), text operations (contains, startswith, endswith, matches), and compound queries (AND, OR).
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Absolute path to the CSV file | |
| limit | No | Maximum results (default: 1000) | |
| query | Yes | Query expression (e.g., status == "active" AND age > 30) | |
| outputFormat | No | Output format (default: records) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It focuses on query syntax rather than behavioral traits like side effects, read-only nature, or return behavior. It fails to disclose that this is likely a non-mutating filter operation.
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, front-loaded sentence that clearly states the purpose and key features without unnecessary detail. It is 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?
The description is adequate but has gaps. It provides query syntax but does not explain return formats or what happens on no matches despite there being no output schema. The schema covers outputFormat, but the description lacks guidance on usage context relative to sibling tools.
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% with descriptions for all parameters, giving a baseline of 3. The description adds meaningful detail about query operators (comparisons, text ops, AND/OR) beyond the schema, which enhances parameter understanding.
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 'Filter records using query expressions' with a specific verb and resource. It lists supported operators, distinguishing it from sibling tools like csv_search and csv_inspect.
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 complex filtering via supported operators but does not explicitly state when to use this tool versus alternatives. No exclusions or comparisons to siblings are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the current cursor position (end of file) for use with csv_tail.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Absolute path to the CSV file |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states the core behavior (getting cursor position) but omits details such as return format, side effects, error behavior, or permission requirements. For a simple read-only tool, this is adequate but incomplete.
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, concise sentence with no wasted words. It front-loads the action and resource, making it easy to scan.
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 with one parameter and no output schema, yet the description does not explain the return value or how to interpret the cursor position. The mention of 'for use with csv_tail' provides some context but not the full picture needed for an agent to invoke it confidently.
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 already provides 100% coverage for the single parameter 'file' with a clear description ('Absolute path to the CSV file'). The tool description adds no further parameter detail, so baseline 3 applies.
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 gets the current cursor position (end of file) with a specific intended use for csv_tail. It uses a specific verb and resource, distinguishing it from sibling tools that operate differently on CSV files.
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 phrase 'for use with csv_tail' indicates when to use this tool (before tailing a file), providing clear context. However, it does not explicitly mention alternative tools or when not to use it, though the purpose is tied to a specific sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get an overview of a CSV file including size, row/column count, detected delimiter, and a preview of the data.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Absolute path to the CSV file | |
| previewRows | No | Number of rows to preview (default: 5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool returns an overview including preview data, implying read-only behavior, but it does not mention performance implications, error handling, or whether it reads the entire file. Enough to understand basic behavior, but not deeply transparent.
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, focused sentence that immediately states the action and key outputs. It is concise and front-loaded with the verb, with no unnecessary detail.
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 moderate tool with 2 parameters and no output schema, the description provides a reasonable overview but lacks details on output format or edge cases. It is adequate for basic understanding but incomplete for advanced usage scenarios, especially given the suite of similar CSV tools.
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%, with file and previewRows already documented in the schema. The description adds no additional parameter semantics; it just lists output features. Baseline 3 is appropriate as 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 tool provides an overview of a CSV file with specific outputs like size, row/column count, delimiter, and preview. The verb 'get an overview' is specific, but it does not explicitly distinguish itself from sibling tools like csv_stats or csv_sample, which can also provide some of these details.
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?
No guidance is given on when to use this tool versus alternatives like csv_sample or csv_stats. The description implies a general overview use case, but with many sibling tools, the lack of explicit exclusion or alternative hints leaves the agent without clear selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List fields in a CSV that contain large values (e.g., base64 images, JSON blobs). Helps identify which fields were truncated in csv_inspect.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Absolute path to the CSV file | |
| threshold | No | Character length threshold for "large" (default: 1000) | |
| sampleRows | No | Number of rows to sample (default: 100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but it only states the action of listing fields and a supporting use case. It does not indicate that the tool reads the file, samples rows, or returns only field names, nor does it mention any side effects. The description is not misleading, but it is thin on behavioral detail, such as whether it is read-only and how results are presented.
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 two crisp sentences, front-loaded with the primary action and containing no filler. Every word earns its place, and the reference to csv_inspect is efficient.
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 has 3 parameters and no output schema; the description explains the purpose and relationship to csv_inspect, but does not describe the return format or sampling behavior. The presence of sampleRows and threshold parameters hints at behavior, but the description doesn't explicitly state that the output is based on a sample, nor does it state expected results. For moderate complexity, this is adequate but leaves 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 schema covers all three parameters with descriptions, so the baseline is 3. The description adds concrete examples of large values (base64 images, JSON blobs) that clarify the 'threshold' semantics, providing context beyond the schema's generic 'character length threshold'.
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 ('list') and identifies the resource ('fields in a CSV') with a clear scope ('that contain large values'). It also differentiates from sibling tools by referencing csv_inspect and noting a specific use case (identifying fields that were truncated).
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 a clear context for use by stating it 'helps identify which fields were truncated in csv_inspect', implying it's a follow-up diagnostic to that tool. It doesn't explicitly mention when not to use or alternative tools, but the intended scenario is clear. It lacks exclusion criteria, but the guidance is more than implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get sample records from a CSV file using various sampling strategies.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Absolute path to the CSV file | |
| mode | No | Sampling mode (default: first) | |
| count | No | Number of records to sample (default: 10) | |
| rangeEnd | No | End line for range mode (1-indexed) | |
| rangeStart | No | Start line for range mode (1-indexed) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must fully disclose behavior. It only says 'get sample records' and 'various sampling strategies,' but does not explain default modes (first, count 10), how range works, whether headers are included, or any edge-case behavior. The schema covers some details, but the description adds little beyond its own purpose statement.
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, compact sentence with no redundant phrases. It is front-loaded with the action ('Get sample records') but could benefit from a brief mention of the modes or output without becoming verbose.
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?
With 5 parameters, 1 required, and no output schema, the description should explain what the tool returns and how the sampling modes behave. It only states the high-level purpose without covering important context like rangeStart/rangeEnd dependencies or default behavior, making it incomplete for 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?
Schema coverage is 100%, so all parameters are described in the schema. The description's mention of 'various sampling strategies' hints at the mode parameter but adds no new meaning beyond the schema definitions. 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 clearly states the tool gets sample records from a CSV file using various sampling strategies, which distinguishes it from siblings like csv_inspect or csv_filter. It could be more specific by naming the strategies (first, last, random, range), but the core purpose is clear and distinct.
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?
No guidance is provided on when to use this tool versus alternatives. It does not mention that csv_tail might be used for last records, or that csv_search is for filtering. There is no exclusionary or comparative information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Infer the schema of a CSV file by sampling records. Returns column names, types, and nullability.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Absolute path to the CSV file | |
| sampleSize | No | Number of records to sample for inference (default: 1000) | |
| outputFormat | No | Output format (default: inferred) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral transparency burden. It discloses that schema inference is done 'by sampling records', which is important for accuracy expectations, and specifies the return content (column names, types, nullability). It does not mention caveats about sampling accuracy or behavior with large files, but adds valuable context beyond the bare 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, well-structured sentence that front-loads the verb and resource, and contains no filler or redundant information.
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 tool's purpose and return values, while the schema covers parameters well. It is somewhat incomplete in that it does not explain the varying output formats (inferred, json-schema, formatted) or discuss limitations of sampled inference, but for a mid-complexity tool this is a minor gap.
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 provides full descriptions for all three parameters, giving 100% coverage, so the baseline is 3. The description's phrase 'by sampling records' aligns with the sampleSize parameter but adds no detailed semantics beyond what the schema already states.
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 ('infer') and resource ('schema of a CSV file'), and notes the return values (column names, types, nullability). This clearly differentiates it from siblings like csv_sample or csv_stats, which focus on sampling rows or computing statistics.
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 the tool should be used when a schema needs to be inferred from a CSV file. However, it gives no explicit guidance on when to prefer this over alternatives like csv_inspect or csv_validate, and provides no exclusions or when-not-to-use context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for records where a field matches a regex pattern.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Absolute path to the CSV file | |
| field | Yes | Field name to search in | |
| pattern | Yes | Regex pattern to match | |
| maxResults | No | Maximum results to return (default: 100) | |
| returnFields | No | Fields to include in results (default: all) | |
| caseSensitive | No | Case-sensitive matching (default: false) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavior. It only states 'search' without explaining matching semantics, result handling (e.g., maxResults, caseSensitive), or error behavior, leaving key operational aspects ambiguous.
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, concise sentence that effectively captures the tool's core purpose. It is front-loaded and contains no redundant information.
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 (six parameters, no output schema, no annotations), the description is too sparse. It does not describe the return format, behavior of optional parameters, or edge cases, leaving the agent with insufficient context to fully anticipate tool behavior.
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 provides 100% coverage of parameters, so the description adds minimal extra meaning beyond the schema. The mention of 'regex pattern' aligns with the 'pattern' parameter but does not elaborate on parameter interactions or formats.
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 for records where a field matches a regex pattern, specifying the verb, resource, and matching criterion. It is specific enough to be useful, though it does not explicitly differentiate from siblings like csv_filter.
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?
No guidance is provided on when to use this tool versus alternatives such as csv_filter or csv_sample. No conditions, prerequisites, or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Collect aggregate statistics for fields in a CSV file. Includes min/max, mean, median, stdDev for numeric fields, and top values for categorical fields.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Absolute path to the CSV file | |
| fields | No | Specific fields to analyze (default: all) | |
| maxRecords | No | Maximum records to analyze (default: all) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It specifies the types of aggregates computed (numeric and categorical) and implies a read-only analysis. It does not mention edge cases or side effects, but the description adds meaningful behavioral detail beyond the schema.
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 two sentences long and front-loaded with the core purpose. Every word adds value, and the list of statistics is precise without unnecessary fluff.
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 has 3 parameters and no output schema. The description clearly communicates the tool's role and the types of statistics returned, but it does not explicitly state the return format (e.g., JSON object). Given the tool's simplicity and clear purpose, this is a minor gap.
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 baseline is 3. The description does not add any additional meaning beyond what the schema already provides for parameters like 'file', 'fields', and 'maxRecords'. It does not elaborate on default behaviors or formatting.
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 function: 'Collect aggregate statistics for fields in a CSV file.' It specifies the exact statistics (min/max, mean, median, stdDev, top values) and distinguishes it from sibling tools like csv_inspect or csv_filter, 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 implies usage by describing what the tool does, but it does not explicitly state when to use it versus alternatives, nor does it mention exclusions. An agent can infer that this is for summary statistics, but explicit guidance is absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read new records appended to a CSV file since a cursor position. Use for monitoring actively-written files.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Absolute path to the CSV file | |
| cursor | No | Byte position to start from (default: 0 = beginning) | |
| timeout | No | Wait time for new content in ms (default: 0 = no wait) | |
| maxRecords | No | Maximum records to return (default: 100) |
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 core behavior (reading from a cursor position) and the monitoring use case, but does not mention cursor advancement, return format, or potential errors. This is a moderate level of transparency.
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 two sentences, with the primary action first and the use case second. Every word earns its place, and it is appropriately sized for the tool's simplicity.
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 has 4 parameters, no annotations, and no output schema, the description is adequate but minimal. It covers purpose and use case but lacks information about return values, cursor handling, and edge cases. It is complete for a simple monitoring tool but leaves some 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 schema has 100% description coverage for all parameters, so the schema already documents each parameter. The description adds a bit of meaning to 'cursor' by explaining the 'since a cursor position' semantics, but this is already implied by the schema's 'Byte position to start from.' No major additional value beyond schema.
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 reads 'new records appended to a CSV file since a cursor position,' which is a specific verb+resource combination. It also distinguishes itself from sibling tools like csv_inspect, csv_sample, and csv_filter by focusing on incremental reads for monitoring.
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?
It explicitly says 'Use for monitoring actively-written files,' providing a clear when-to-use context. It does not mention alternatives or exclusions, but the use case is sufficient for a tool of this simplicity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate a CSV file for syntax errors and optionally against a schema.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | Absolute path to the CSV file | |
| schema | No | Schema to validate against | |
| maxErrors | No | Maximum errors to report (default: 100) | |
| stopOnFirstError | No | Stop on first error (default: false) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states the core behavior but doesn't disclose whether files are modified, what the return format is, or how errors are reported. Validating generally implies read-only, but this is not explicit.
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 concise sentence, front-loaded with the purpose, and contains no filler or redundant information.
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 has moderate complexity with nested schema parameters but no output schema. The description does not explain return values, error reporting, or validation report structure, which is a gap given the absence of an output schema.
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 all parameter details are in the schema. The description adds minimal context beyond the 'optional schema' mention, providing no additional semantic value over the schema.
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 validates a CSV file for syntax errors and optionally against a schema. The verb 'Validate' is specific, the resource is explicit, and it differentiates from sibling tools like csv_inspect or csv_filter.
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 when to use the tool: when you need to check CSV syntax or validate against a schema. It doesn't explicitly mention alternatives or exclusions, but sibling tools have distinct purposes, making context clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0csv_diffcsv_extractcsv_filtercsv_get_cursorcsv_inspectcsv_large_fieldscsv_samplecsv_schemacsv_searchcsv_statscsv_tailcsv_validateScored across 12 tools
Tools are largely distinct with clear purposes. csv_search and csv_filter both retrieve matching records but differ in query syntax (regex vs expression), which could cause some initial confusion. csv_inspect and csv_schema provide different levels of overview, but descriptions clarify the difference.
All tools share the csv_ prefix and snake_case, creating a recognizable family. However, naming style is inconsistent: some use verbs (csv_filter, csv_validate), some nouns (csv_schema, csv_stats), and some adjective_noun (csv_large_fields). This slight inconsistency prevents a perfect score.
At 12 tools, the count is well-suited to the server's purpose of comprehensive CSV exploration. Each tool addresses a distinct aspect, and the count is neither sparse nor bloated.
The tool set covers the core lifecycle of CSV analysis: inspection, sampling, schema, stats, search/filter, validation, monitoring, diffing, and handling large fields. Minor gaps include lack of sorting or column manipulation, but these are beyond the apparent scope.
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