OSCAR MCP Server

by Barbaroso

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct resource or action: metadata (describe_database, list_profiles, list_channels), device info, summaries, statistics, nightly details, events, settings, and session details. Even run_sql is clearly scoped as a fallback for analyses not covered by the specialized tools, and descriptions clarify boundaries between similar tools like get_daily_detail vs get_session_details.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (run_sql, list_profiles, get_device_info, list_channels, describe_database, get_daily_summaries, etc.). The verbs are predictable (list/get/describe/run) and nouns are clear, making the API easy to navigate.
Tool Count5/5
With 11 tools, the server is well-scoped for the domain of OSCAR CPAP data analysis. Each tool covers a necessary part of the workflow (profiles, schema, raw queries, summaries, statistics, details, events, settings, sessions) without redundancy or bloat.
Completeness5/5
The tool surface provides comprehensive coverage for reading and analyzing therapy data: profile discovery, device info, channel mapping, database schema, nightly summaries, period statistics, per-night detail, event-level data, settings history, and session-level stats. The inclusion of run_sql fills any potential ad-hoc analysis gaps, leaving no obvious dead ends.
Average 3.5/5 across 11 of 11 tools scored. Lowest: 2.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 9 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under GPL 3.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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?
With no annotations, the description must carry the burden of disclosing behavioral traits. It indicates aggregation but does not explicitly state read-only behavior, output format, default date handling, or any permissions. 'Aggregate statistics' implies a safe read operation, but that is not confirmed.
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 a clear leading verb and a compact list of statistics categories. It avoids filler and is front-loaded, though the enumeration is somewhat dense.
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 no output schema and no annotations, the description offers a useful high-level overview but omits parameter semantics, return shape, and boundary behaviors. It is insufficient for a tool with three undocumented parameters and several sibling tools covering different granularities.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage and no per-field descriptions. The description only mentions 'a period', loosely mapping to start_date and end_date, but does not explain the profile parameter, date format, or default behavior. It fails to compensate for the schema gap.
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 specific verb ('Aggregate statistics') and a resource ('for a period'), and enumerates the content areas included (usage, compliance, AHI distribution, event totals, pressure/leak summaries, trends). It is reasonably clear, though it does not explicitly distinguish itself from the sibling get_daily_summaries tool.
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 explicit when-to-use or when-not-to-use guidance is provided. The phrase 'for a period' implies use for period-level statistics, but with siblings like get_daily_summaries and get_daily_detail, the description should state when this aggregate tool is preferred over those alternatives.
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 of behavioral disclosure. It only states the general purpose and the effect of the changes_only parameter, but does not disclose output format, default behavior, date range handling, or any other operational traits.
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 exactly two sentences, front-loaded with the tool's core purpose, and contains no redundant words. Every phrase contributes value.
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 four optional parameters, no annotations, and no output schema, the description is too sparse to set complete expectations about return values, pagination, or default date ranges. The bare-bones nature leaves significant gaps for an agent trying to invoke 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?
Schema coverage is 0%, so the description is the only source of parameter meaning. It explains changes_only well, but profile, start_date, and end_date are left to be inferred from their names, which is reasonable but not fully compensating for the lack of schema descriptions.
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 retrieves machine settings over time and explicitly lists the setting types (pressure limits, EPR, mode, ramp, humidity). It is specific enough to distinguish from sibling tools, though it does not explicitly contrast itself with alternatives as in the get_calls example.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives like get_daily_summaries or get_device_info. It only offers a parameter hint about changes_only, which is not equivalent to usage 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 carries the full burden of behavioral disclosure. It discloses the return content (therapy results fields) and the date-handling behavior (ISO format, omit for all data). However, it does not explain the effect of limit or profile parameters, or whether results are ordered or paginated. The level of detail is moderate 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence packed with essential information. It front-loads the main purpose and adds a concise note on date handling. Every word earns its place, with no unnecessary filler.
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 4 parameters, no output schema, and no annotations, making description completeness crucial. While it covers the output fields and date parameters, it omits the behavior of limit and profile, and lacks any mention of ordering or pagination. For a 4-parameter tool with no output schema, this is insufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains the start_date and end_date behavior via the note on ISO format and 'omit them for all data', but it does not explain the limit or profile parameters at all. Half the parameters remain undocumented in both the schema and description, leaving significant ambiguity.
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 returns a night-by-night table of therapy results with specific metrics (usage hours, AHI, event counts, pressure, leak). It is specific and informative, but it does not explicitly distinguish this tool from siblings like get_daily_detail or get_statistics, so it lacks 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 does not provide guidance on when to use this tool versus alternatives like get_daily_detail or get_statistics. It only gives a note about date formats and omitting them for all data, which is parameter usage, not tool selection guidance. No exclusions or alternatives are mentioned.
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 of behavioral disclosure. It only mentions output fields but does not disclose read-only nature, behavior for null or invalid profiles, empty result handling, or error conditions. This is a significant gap.
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 sentence, concise and front-loaded with the action and object. It contains no fillers or redundancy, making it easy to parse and understand.
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 there is no output schema and no annotations, the description should explain the return structure and edge cases. It only mentions two output fields but does not specify whether a list is returned, how null is handled, or what happens on error, leaving the description incomplete for reliable invocation.
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 description clarifies that the 'profile' parameter refers to the profile whose devices are fetched, adding meaning beyond the bare schema name. However, it does not explain the null default or expected format (e.g., ID vs name), so compensation for the 0% schema description coverage is only partial.
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 ('Get'), the resource ('therapy devices recorded for a profile'), and the output scope ('including model and last import time'). This distinguishes it from sibling tools that focus on summaries, settings, or sessions.
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, no prerequisites, and no exclusions. It only states what the tool does, leaving the agent to infer usage context without any explicit direction.
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 states what the tool returns and does not mention read-only nature, permissions, error conditions, or output format, leaving an agent without critical safety and expectation details.
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 redundancy. Every word adds meaning and the structure is optimal for quick scanning.
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 leaves the agent uncertain about the exact output and the optional profile parameter. No output schema or annotations exist to fill gaps, so the description should provide more detail about what 'per-channel statistics' includes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains the session_db_id parameter's role, but it completely omits the profile parameter. With 0% schema description coverage, the description should compensate for both parameters, which it fails to do.
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 returns per-channel statistics for a single session, identifying the session_db_id as the key. It is distinct from siblings like get_daily_summaries by scope, though it does not explicitly differentiate from get_statistics.
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 description implies usage when a session_db_id is known and channel-level detail is needed. It provides clear context but does not explicitly exclude alternatives or mention when to prefer this over other statistics tools.
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?
Without annotations, the description carries the burden for behavior. It adds that large leak spans are reported separately, which is useful, but it does not disclose side effects, read-only nature, or the impact of the include_events parameter. The description provides some transparency but leaves key behavior unspecified.
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 two sentences with no filler. It front-loads the core purpose and adds a brief behavioral note. Every word earns its place.
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 3 parameters, no annotations, and no output schema. The description covers the output shape in part but leaves parameter semantics and usage context unaddressed. For a tool with multiple parameters and no schema documentation, the description is not complete enough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% coverage and the description does not explain any parameters. 'one night' hints at the date parameter, but there is no mention of profile or include_events. The agent cannot infer what include_events toggles or what profile means.
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 provides individual respiratory events (apneas, hypopneas, RERAs) for one night, along with counts by type and an hourly histogram. This specific scope distinguishes it from siblings like get_daily_summaries or get_daily_detail.
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 given on when to use this tool versus alternatives. The note about large leak spans being reported separately is a behavior nuance, not a usage context. The tool does not mention exclusions or alternatives.
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 discloses the scope of returned data and the ISO date requirement, but does not explain the optional profile parameter's effect, pagination, or the output structure. The read-only nature is implied by 'get' but not explicitly stated.
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 compact and front-loaded, using two short sentences to convey the tool's content and a key constraint. Every phrase adds value without unnecessary verbosity.
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?
Given the absence of an output schema and annotations, the description reasonably outlines the categories of returned data. However, it omits profile parameter semantics and any relationship to sibling tools, leaving gaps in what an agent can infer about output format and filtering behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. It explains the date parameter's ISO format but says nothing about the optional profile parameter, its allowed values, or how it filters results. This leaves a required parameter half-documented.
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 what the tool does: it retrieves all recorded data for one night, including sessions, machine settings, event counts, and per-channel statistics. This distinguishes it from siblings like get_daily_summaries and get_session_details by emphasizing the comprehensive detail scope.
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 gives no explicit guidance on when to use this tool versus alternatives such as get_daily_summaries or get_session_details. The date format requirement is mentioned, but there is no comparison, exclusion, or context about when the detailed view is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context about mapping IDs to names, but it omits key behavioral details such as the default filtering via only_used=true (default) and the meaning of profile. Without annotations, these gaps are significant, though the tool is likely read-only.
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, well-structured sentence that front-loads the action ('List') and includes relevant examples without unnecessary padding.
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 list tool, the description covers the main purpose but is incomplete given no annotations or output schema. It doesn't explain return format (beyond mapping), default behavior, or parameter semantics, leaving the agent to infer from parameter names and defaults.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%; the description does not explain the 'profile' or 'only_used' parameters. It only hints at channel names, which concerns output, not parameters. The description fails to compensate for the lack of parameter documentation.
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 function: listing OSCAR-recorded data channels and mapping numeric IDs to human-readable names. It includes specific examples (AHI, Pressure) and is distinct from siblings like list_profiles or get_daily_summaries.
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 description implies use when needing channel names or interpreting numeric IDs. It doesn't explicitly state alternatives or when not to use it, but the purpose is clear enough to guide selection among sibling tools.
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?
There are no annotations, so the description carries the full burden of behavioral disclosure. The verb 'List' implies a read-only operation, and 'data coverage' gives useful context about the output. However, it does not explicitly state that the tool has no side effects or whether it makes external calls. For a simple list operation, this is adequate but not highly transparent.
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 two short sentences: the first states what the tool does, the second provides usage context. Every word earns its place, and the information is front-loaded with the verb and resource first. No fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no output schema, the description sufficiently states the return content ('profiles with their data coverage') and provides strong usage context ('call this first'). Given the low complexity and zero params, the description is complete and leaves no major gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema is empty. With 0 params, the baseline is 4, and the description adds no parameter-specific details because none are needed. The mention of 'data coverage' is not parameter-related but enriches the overall understanding.
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 the specific verb 'List' with a clear resource ('OSCAR profiles') and adds the scope 'with their data coverage'. This distinguishes it from sibling tools like list_channels and describe_database, which target different resources. The purpose is immediately understandable.
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 description provides explicit guidance: 'Call this first when you do not know which profile or date range is available.' This makes the primary use case clear. It does not mention alternatives or when not to use, but the 'call this first' placement strongly implies it is the entry point for discovery.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden. It explicitly states read-only nature, permitted statement types (SELECT/WITH), time-budget cancellation, and the schema pitfall where wrong join keys return zero rows. These are valuable behavioral insights beyond what the schema or annotations would show.
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 three sentences, each serving a distinct purpose: stating the overall purpose, listing constraints, and giving usage guidance with alternatives. There is no redundancy or filler; every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers critical safety and operational context (read-only, timeout, schema ambiguity) and directs users to specialized tools where appropriate. However, it does not mention the return format or any authorization requirements, which would be useful for a generic SQL tool with no output schema.
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 description adds meaning for the 'sql' parameter by restricting permitted statements and warning about join keys, but it does not explain the 'limit' parameter or provide examples of valid SQL formatting. Since schema description coverage is 0%, the description only partially compensates for the undocumented parameters.
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 states 'Run a read-only SELECT against the OSCAR database for analysis the other tools do not cover,' which clearly identifies the action, resource, and scope. It also distinguishes this tool from specialized siblings by presenting it as the generic fallback for uncovered analyses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is provided: 'Call describe_database first' is a clear prerequisite, and 'For mask, mode and other categorical settings prefer get_therapy_settings' names a specific alternative. The phrase 'analysis the other tools do not cover' defines when to choose this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It is a describe operation, inherently read-only, and the text clarifies what information is returned (tables, columns, etc.), adding behavioral context. However, it does not mention potential latency, output size, or that it makes no changes, but the nature of the tool makes this less critical.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, the first states the core purpose with examples, the second gives actionable guidance. No wasted words; front-loaded and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description fully covers what the tool provides: a comprehensive schema overview plus join/decoding rules. The explicit link to run_sql completes the context, making it self-sufficient 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.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so baseline 4 applies. The description adds no parameter info, but none is needed. It appropriately focuses on what the tool does rather than parameters.
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 describes the OSCAR database, listing specific components (tables, columns, foreign keys, join and decoding rules). It names run_sql as the sibling tool meant to be used afterward, effectively distinguishing its purpose as a preparatory schema-inspection tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs the agent to read this before writing run_sql, providing a clear usage context and relationship to the sibling tool. This is a direct 'when to use' guideline, fulfilling the dimension.
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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