Server Quality Checklist

Tool Scores

• get_stock_quoteB
  3.4/5.0
  Behavior2/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations provided, the description must fully disclose behavioral traits. It only states the data obtained (price, change, volume) but omits critical details such as data freshness (real-time vs delayed), error handling for invalid symbols, rate limits, or whether the data is from a live or simulated source. This is insufficient for reliable agent decision-making.

  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 that front-loads the key outputs. Every word is informative; no fluff or repetition. It achieves maximum conciseness while conveying essential functionality.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's simplicity (one parameter, clear output), and the presence of an output schema (context signal), the description is mostly complete. It covers what the tool returns. However, it lacks information about data sources or freshness, which slightly reduces completeness. Still, it is adequate for a straightforward stock quote lookup.

  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 description coverage is 100% for the single parameter 'symbol'. The description adds no additional meaning beyond the schema's 'Stock ticker symbol, e.g. NVDA'. Per guidelines, with high coverage, baseline is 3. No extra value provided.

  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 verb 'Get' and the resource 'latest price, daily change %, and trading volume for a stock'. It distinguishes from sibling tools: compute_rsi (RSI indicator), get_historical_prices (historical data), and generate_research_report (research). The purpose is specific and unambiguous.

  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 its siblings. It does not mention that this is for current data vs historical data (get_historical_prices) or technical analysis (compute_rsi). The agent must infer usage from the tool names alone.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• get_historical_pricesA
  3.6/5.0
  Behavior3/5

  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 correctly implies a read operation ('Retrieve'), but does not disclose other behavioral traits such as authentication requirements, rate limits, or whether the data is cached. For a simple historical data tool, this is adequate but not comprehensive.

  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 with no unnecessary words. It is front-loaded and efficient.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given that an output schema exists, the description does not need to explain return values. It covers the core action and scope. However, it could mention that the data is historical and that interval is optional, but the schema handles that. Overall, it is complete for a tool of this complexity.

  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 100%, so the baseline is 3. The description adds no additional meaning beyond the schema; it mentions 'OHLC price history' but does not elaborate on the specific fields returned. The schema already documents parameters with enums and defaults, so the description is sufficient but not exemplary.

  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 ('Retrieve'), the resource ('OHLC price history'), and the scope ('for a stock over a given period'). It effectively distinguishes from siblings like get_stock_quote (current price) and compute_rsi (derived indicator).

  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 when not to use it, such as for current prices (use get_stock_quote) or for technical indicators (use compute_rsi).

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• compute_rsiA
  3.7/5.0
  Behavior4/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations are provided, so the description carries full burden. It discloses the output structure (RSI value, signal with thresholds: overbought, oversold, neutral) and implies it is a read-only computation. It does not mention potential side effects or auth, but for a stateless computation this is acceptable.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is concise (4 lines) and front-loaded with the key action. The formatting with blank lines is slightly wasteful but does not hinder readability. Every sentence adds value.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a simple 2-parameter tool with an output schema, the description adequately explains the return value structure (RSI value and signal classification). It is complete enough for an AI agent to understand the tool's function and outputs.

  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 100% with descriptions for both parameters (symbol, period). The description does not add any new parameter semantics beyond the schema, but it is not required to. Baseline 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 states 'Compute the Relative Strength Index (RSI) for a stock,' which is a specific verb+resource. It clearly distinguishes this tool from siblings like get_historical_prices or generate_research_report by focusing on a single technical indicator.

  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 guidance is provided on when to use this tool vs alternatives (e.g., when to use RSI over other indicators, or when to use this tool instead of generating a full research report). The description only explains the output signal thresholds, not the broader decision context.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• generate_research_reportA
  4.5/5.0
  Behavior5/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations are provided, so the description carries full burden. It transparently describes concurrent fetching, streaming via MCP progress notifications, token-by-token output, and return of complete text. This provides a clear understanding of behavior.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is concise, with the first sentence stating the purpose, followed by details on process and output. No wasted words; structured for readability.

  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?

  With an output schema present, description does not need to detail return values. It covers input (symbol), process (concurrent fetches, streaming), and output (complete report). Adequate for a single-parameter tool with high schema coverage.

  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% (the schema includes 'Stock ticker to research, e.g. NVDA'). The tool description adds no additional meaning beyond the schema. Baseline 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 'Generate a streaming AI research report for a stock.' The verb 'generate' and resource 'research report' are specific. It distinguishes itself from sibling tools (compute_rsi, get_historical_prices, get_stock_quote) by being a higher-level composite tool.

  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: for a comprehensive report, use this tool. However, it does not explicitly state when to use it versus siblings or provide exclusion criteria. The context is clear but not explicit.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.