mcp-server-duckdb

by SyneHQ

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v0.0.3
Disambiguation4/5
The create_table_from_* family is distinguished by source type (path, URL, S3, CSV), though path and CSV may overlap since a CSV is a file path. query and inspect_query are distinct (execution vs inspection), and the rest target different resources.
Naming Consistency3/5
Most tools follow a verb_noun pattern in snake_case, but there are deviations: query is just a verb, is_table_exists uses a question-style, and smart_load_multiple_csv_files breaks the create_table_from_* convention despite similar CSV loading.
Tool Count5/5
Twelve tools is well within the recommended range and covers a focused set of database operations: table creation from various sources, querying, inspection, and metadata. Each tool appears purposeful.
Completeness4/5
The set covers loading, querying, describing, listing, and exporting tables. Missing explicit drop/alter operations, but the query tool can execute any SQL, mitigating this gap. Minor gap: no batch export or schema listing beyond describe.
Average 3.1/5 across 12 of 12 tools scored. Lowest: 2.3/5.
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
- Last stable release on April 14, 2025
- 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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}
```
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether the query is executed, whether it returns a plan or statistics, or any side effects. 'Inspect' hints at read-only but is insufficient.
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 a single concise sentence with no wasted words. However, its brevity contributes to vagueness, so it is not a 5—it is under-specified but structurally efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple tool with one string parameter and no output schema, so the description must compensate by explaining what 'inspect' means (e.g., returns EXPLAIN plan, validates syntax). It provides none of that, leaving the agent unable to predict behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema fully documents the only parameter 'query' with the description 'SQL query to inspect'. The tool description adds no additional meaning beyond the schema, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the verb 'inspect' which is ambiguous—it could mean validate, explain, or execute. While it names the resource (a query in DuckDB), it does not distinguish from the sibling tool 'query' clearly.
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 is provided about when to use this tool instead of alternatives like 'query' or 'describe_table'. There is no mention of prerequisites, context, or exclusions.
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 is the sole source of behavioral information. It only states the action without explaining side effects, required permissions, the role of the 'replace' parameter, or whether the table is created in a specific workspace or database.
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 a single, front-loaded sentence with no filler or repetition. 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations and no output schema, the one-sentence description is insufficient for a 3-parameter tool. It does not explain edge cases, file format support, or the behavior of the 'replace' parameter, making it incomplete for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully describes all parameters (coverage 100%), so the baseline is 3. The description adds no additional meaning beyond what the schema already provides, such as path semantics or the effect of 'replace'.
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 the action (create) and the resource (a table from a file path), which distinguishes it from siblings like create_table_from_url and create_table_from_s3. However, it does not specify which file formats are supported, creating ambiguity with create_table_from_csv.
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 is provided about when to use this tool over alternatives. There is no mention of local file paths vs. URLs or S3, nor 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of explaining behavior. It only says 'create', which implies a write operation, but does not disclose the effect of the 'replace' parameter, whether the operation is potentially destructive, network dependencies, or any side effects. This is minimally transparent for a mutation tool.
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 a single sentence with no wasted words, making it concise. However, it is perhaps too brief for a tool with 3 parameters and no annotations, and it does not structure any additional context. Still, it is appropriately front-loaded and succinct.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no annotations, and no output schema, the description is incomplete. It does not explain what happens on success, how the 'replace' flag affects behavior, supported file formats, or error conditions. The one-line description leaves significant gaps for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with descriptions for all three parameters (url, table, replace). The description adds no additional semantic value beyond what the schema already provides, so the baseline of 3 is appropriate.
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 'Create a table from a URL' clearly states the action (create) and the resource (table) and the source (URL), which is specific enough to differentiate from siblings based on source. However, it does not explicitly reference alternative tools like create_table_from_path or create_table_from_s3, so it lacks direct 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 Guidelines2/5
Does 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 exclusions. The only implied usage is 'from a URL', but this is essentially restating the tool's purpose and offers no decision-making support.
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?
No annotations are provided, so the description must carry the burden of behavioral disclosure. It does not mention whether files are overwritten, if directories are created, if permissions are needed, or the default format (PARQUET) from the schema. This is a significant gap for a mutation/export tool.
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 a single, concise sentence with no fluff. It is front-loaded and efficient, but it is perhaps too sparse to fully inform the agent. Still, it earns a high score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, no annotations, and 3 parameters, the description is insufficiently informative. It does not clarify whether 'path' is a file path or directory, whether the tool creates directories, or what formats are supported beyond the schema's default. These gaps would likely confuse an agent selecting or invoking the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides complete descriptions for all three parameters (path, table, format) with a default value, so the baseline is 3. The description adds no extra semantic value 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?
The description clearly states the action (export) and object (a table) plus destination (a file). It distinguishes from siblings, which are import/query tools. However, it lacks specifics about the file format or path semantics, which slightly reduces clarity.
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?
There is no guidance on when to use this tool versus alternatives. The description does not mention any context, prerequisites, or exclusions, leaving the agent to infer when export is appropriate.
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 bears full responsibility for behavioral disclosure. It only states 'Execute a query', which essentially repeats the tool name. It does not reveal whether the query can mutate data, what the return format is, whether errors are handled, or any side effects. This is minimal and inadequate for a database execution tool.
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 a single, front-loaded sentence: 'Execute a query on the DuckDB database'. Every word earns its place, and there is no unnecessary information. It is highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has one parameter, no output schema, and no annotations. The description does not explain return values, query execution semantics, whether DDL/DML is allowed, or how it differs from related tools. Given the minimal context, the description leaves significant gaps for an agent attempting to use this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema contains one parameter with full 100% description coverage: 'SQL query to execute'. The description adds no additional meaning beyond the schema, so the baseline of 3 applies. The schema already clearly documents the parameter's purpose.
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 states a specific action: 'Execute a query on the DuckDB database'. It clearly identifies the resource (DuckDB) and the operation (execute a query). However, it does not distinguish itself from the sibling tool 'inspect_query', which also involves queries, so it misses the differentiation needed for a 5.
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?
There is no guidance on when to use this tool versus alternatives such as inspect_query or the table creation tools. The description gives no context about appropriate use cases, prerequisites, or exclusions, leaving the agent without decision support.
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 carries the full burden of behavioral disclosure. It does not mention whether the operation is read-only, what the output format looks like, or any side effects. The description is purely functional with no behavioral context.
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 a single, short sentence that is immediately clear and contains no filler. It is appropriately concise for a simple one-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is minimal and lacks important operational details such as what specific statistics are returned, error handling for nonexistent tables, and whether it is safe to call repeatedly. Given the absence of annotations and output schema, this leaves the agent with significant ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage for the single parameter 'table' with the description 'Name of table to summarize'. The tool description adds no additional semantic information beyond this, so the high schema coverage establishes a baseline of 3.
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 the tool's function—'Get summary statistics for a table'—using a specific verb and resource. It is distinguishable from sibling tools like describe_table (which likely returns schema) and query (custom SQL), though it doesn't explicitly call out these differences.
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 is provided on when to use this tool versus alternatives. The description simply states what it does, leaving the agent to infer that it should be used when summary statistics are desired, without any exclusions or comparisons.
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?
No annotations are provided, so the description carries the full burden. It only restates the tool's name function and does not disclose that the operation is read-only, what the returned description includes, or any error conditions.
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 a single, focused sentence with no wasted words. Every word contributes to conveying the tool's core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read-only tool, the description is minimal but lacks context about the output format and relationship to sibling tools like summarize_table. It is adequate but has clear gaps in helping the agent understand what 'describe' actually returns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents the 'table' parameter with 100% coverage (name and description). The tool description adds no extra semantic detail beyond what the schema provides, so a baseline score of 3 is appropriate.
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 uses a clear verb ('Describe') and resource ('table in the DuckDB database'), making the primary action evident. However, it does not distinguish 'describe' from the sibling 'summarize_table', which could create ambiguity about which tool to use.
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 when-to-use guidance or alternatives are provided. The description does not clarify when to choose describe_table over show_tables or summarize_table, leaving the agent without decision support.
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 carries the full burden. It only states the basic action of creating a table; it does not disclose permissions, potential side effects (e.g., overwriting), or error behavior, which is especially important for a mutation operation.
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 a single concise sentence that directly states the purpose without any unnecessary words. It is appropriately front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool is a mutation with no annotations and no output schema, the description is too sparse. It fails to explain the resulting table's structure, any prerequisites, failure modes, or how it compares to similar tools. The schema covers parameters but not the broader context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters. The description adds no additional meaning beyond what the schema already conveys, meeting the baseline for high coverage.
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 'Create a table from an S3 path' uses a specific verb and resource, and clearly distinguishes itself from sibling tools like create_table_from_url or create_table_from_csv by indicating the source as S3 path.
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 is provided on when to use this tool versus the sibling tools (e.g., create_table_from_path, create_table_from_url). The description lacks any context about preferred scenarios or exclusions.
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 carries the full burden for behavioral disclosure. It only restates the name, providing no details on return format, side effects (none expected), or that views are excluded by default. This is insufficient for a read-only listing tool.
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 a single, concise sentence that front-loads the core purpose without unnecessary words. It is appropriately sized for such a straightforward tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description should clarify the return value and mention the include_views option. It does neither, leaving the tool under-specified for a complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers the only parameter include_views with a description, so the baseline is 3. The tool description adds no additional meaning about this parameter and does not mention the option to include views.
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's purpose with a specific verb and resource: 'Show all tables in the DuckDB database'. This distinguishes it from sibling tools that create, summarize, or export tables.
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 is provided on when to use this tool versus alternatives. Sibling tools like describe_table, is_table_exists, and inspect_query are not mentioned, leaving the agent without context on selection criteria.
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?
No annotations are provided, so the description must disclose behavioral traits. It only states the basic create action and does not mention behavior such as handling of existing tables, delimiter inference, or schema inference. This is insufficient for a potentially 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no redundant wording. It conveys the core purpose efficiently without unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple create-from-CSV tool, the description identifies the source and action adequately, but it lacks additional context such as error behavior, CSV format assumptions, or what happens if the table already exists. Given the absence of an output schema and annotations, a bit more detail would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters (path, table, delimiter) are fully described in the input schema with 100% coverage. The description adds no additional parameter semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.
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 action (create) and resource (table) with a specific source (CSV file). It distinguishes from sibling tools such as create_table_from_path, create_table_from_url, and create_table_from_s3 by the source type.
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 does not explicitly mention when to use this tool versus alternatives, but the use case is implied by the tool's name and the CSV source in the description. There is no explicit alternative or exclusion guidance.
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, the description must carry the behavioral burden. It clearly implies a read-only existence check but does not disclose the return format (e.g., boolean), error handling, or schema scope. Some key behavioral context is missing.
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?
A single, direct sentence with no filler. Front-loaded with the action and resource.
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 one-parameter existence check, the description is mostly complete. However, without an output schema or annotations, it omits the return value and any edge cases, leaving a small but notable gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already fully documents the single 'table' parameter as 'Name of table to check'. The description adds only the database context, not additional parameter semantics. Baseline 3 applies because schema coverage is 100%.
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 uses a specific verb ('Check if...exists') and names the resource ('table in the DuckDB database'). It clearly distinguishes this from sibling tools like show_tables or describe_table.
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 is provided on when to use this tool versus alternatives. It does not mention that show_tables lists tables or that this is useful for conditional logic. The use case is only implied.
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, the description carries full behavioral burden. It discloses the key behavior of content-based naming, which goes beyond the schema, but leaves unspecified details like error handling, overwriting behavior, and the exact nature of the content analysis. The mutating nature is implied by 'name the tables'.
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?
A single sentence with a clear verb and object structure; no redundancy or filler words. It is front-loaded and immediately informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is straightforward with complete parameter documentation, but without annotations or an output schema, the description omits details on return values and failure modes. The 'content analysis' behavior is mentioned but not elaborated, leaving some contextual gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers both parameters (paths and delimiter) at 100%, so the description adds little beyond implying that 'multiple' refers to the paths array. The schema already documents the parameters sufficiently, justifying the baseline score.
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 loads multiple CSV files and names resulting tables based on content analysis, using the specific verb 'Load' and identifying both the resource (CSV files) and the outcome (tables). It implicitly distinguishes itself from sibling tools like create_table_from_csv by focusing on multiple files and intelligent naming.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'multiple CSV files' provides clear context for when to use this tool over single-file alternatives, and 'intelligently name' signals its unique benefit. However, it does not explicitly name alternatives or state exclusions, so it falls short of full usage guidance.
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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