OptionContext
Server Quality Checklist
Latest release: v0.1.3
- Disambiguation5/5
Each tool serves a clearly distinct purpose: market context for a single symbol, batch market context, option chain, historical data, symbol search, and API key info. The single versus batch distinction is clear, and option chain/historical data are fundamentally different data types.
Naming Consistency4/5All tools use lowercase snake_case, and five of six follow a consistent 'get_' prefix pattern. The exception is 'suggest_symbols', which still uses a verb but deviates from the 'get_' convention, making it slightly inconsistent.
Tool Count5/5Six tools is well within the ideal range for a market data server. Each tool covers a necessary function without unnecessary bloat, making the count appropriate for the server's stated purpose.
Completeness5/5The tool surface covers the core needs of a market data context server: live quotes (single and batch), option chains, historical data, symbol resolution, and API key validation. No obvious dead ends or missing operations for the intended use case.
Average 4.2/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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
- 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 mentions the subscription requirement, which is helpful, but does not explicitly discuss read-only nature, potential errors, rate limits, or pagination. The output schema exists, so return format is covered, but behavioral edge cases are under-specified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact: one clear purpose sentence followed by a structured parameter list with no fluff. The most important information is front-loaded, and every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers all parameters and the subscription requirement, and an output schema exists. However, it omits details like timezone handling and whether date ranges are inclusive, leaving minor gaps for a data-fetch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description lists all five parameters with formats and allowed values, e.g., interval options ('ONE_MINUTE', 'FIVE_MINUTE', etc.) and date format (YYYY-MM-DD). This fully compensates for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Fetch historical daily/intraday OHLCV candles', clearly stating the action and resource. This distinguishes it from sibling tools like get_option_chain or get_market_context, making the tool's purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description notes 'Requires Plan B subscription', providing a clear prerequisite, but it does not explicitly mention when to prefer this over alternatives like get_batch_market_context. Usage context is implied by the tool name and description, but explicit when-to-use/when-not-to-use guidance is missing.
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 burden of disclosing behavior. It adds useful context about the optional api_key fallback to an environment variable, and the 'complete' matrix implies read-only data retrieval. However, it does not mention potential errors, rate limits, or other behavioral constraints, so it is only partially transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a single clear sentence stating the tool's purpose followed by a neatly formatted Args block. Every sentence contributes value with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, an output schema, and only two parameters, the description covers the core requirements: purpose, parameters, and authentication. It does not discuss edge cases or explicit differences from siblings, but it is sufficiently complete for the tool's scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no property descriptions (0% coverage), but the description's Args section fully explains both parameters with types, purpose, and an example for symbol, plus the api_key behavior. This fully compensates for the schema's lack of semantic detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a complete option chain matrix and lists key included elements (PCR, ATM strike, CE/PE quotes, volume, Greeks), making it specific and easily distinguishable from sibling tools like get_market_context or get_historical_data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when option chain data is needed and provides example symbols, but it does not explicitly state when to use it versus alternatives or include any exclusions. This is adequate but lacks clear guidance on tool selection.
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 of behavioral disclosure. It states that the tool 'resolves' symbols, but it does not disclose the return format, whether it returns a single match or a list of suggestions, or any potential errors. The example implies a mapping but lacks explicit behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—only two sentences plus a one-argument block. It front-loads the purpose and includes a relevant example without any fluff. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with an existing output schema, the description is nearly complete. It lacks explicit usage guidance and return-format details, but the low complexity and clear purpose make it adequate in context. The example and parameter explanation cover the essential usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only declares 'query' as a string with no description. The description's Args section provides full semantic meaning: 'The search text or company name.' This fully compensates for the 0% schema coverage, and the example further illustrates acceptable input.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Search and resolve' with a clear resource 'correct ticker symbols', and the example 'Reliance' to 'RELIANCE' clarifies the exact function. This tool is clearly distinct from sibling market-data tools like get_option_chain or get_historical_data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear use case with an example and the parameter explanation, indicating that the tool is for turning natural-language company names into ticker symbols. It does not explicitly state when not to use it or mention alternatives, but the purpose is strong enough that an agent would know when to invoke it versus fetching market data.
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 burden for behavioral disclosure. It reveals defaults for expiry and asset_type and the api_key environment fallback, but does not address potential side effects, rate limits, error handling, or explicitly confirm read-only behavior beyond the word 'Fetch'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a one-sentence summary followed by a compact Args list. It is front-loaded with the tool's purpose and contains no redundant text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, return values are already specified. The description covers all inputs: symbol, expiry, asset_type, and api_key, with defaults and one required param. It provides enough context for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no descriptions, but the description fully compensates by explaining each parameter: symbol as ticker, expiry with allowed values and default, asset_type with allowed values and default, and api_key optional with environment fallback. This substantially exceeds schema information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches live quote details, LTP, volume, and context for Stocks, Indices, and Futures, using a specific verb and resource. This distinguishes it from siblings like get_option_chain and get_historical_data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for live market context but does not explicitly mention when to use it over alternatives like get_batch_market_context or get_historical_data. It provides no exclusions or comparisons, so the agent must infer usage from the tool's scope and sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It adds useful behavioral context: it mentions 'live' data, optional api_key with environment fallback, and the default expiry. However, it does not disclose rate limits, error handling, or side effects. This is a moderate level of disclosure, 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: a single purpose sentence followed by a well-structured Args list. Every sentence contributes necessary information, and the structure is clean and front-loaded with the primary purpose. No redundant text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists, the description does not need to explain return values. It covers the core invocation details: what the tool does, all parameters with defaults, and authentication behavior. However, it does not mention limits on batch size or explicitly clarify the distinction from sibling tools, which leaves small but meaningful gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description is the only source of parameter meaning. It thoroughly documents each parameter: symbols with an example, expiry with allowed values and default, and api_key with optionality and environment fallback. This adds significant value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Fetch live quote details, last traded price (LTP), volume, and context for a batch of Stocks, Indices, or Futures.' It uses a specific verb ('Fetch') and a resource ('batch market context') and explicitly differentiates from the sibling tool get_market_context by emphasizing 'batch'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for multiple symbols through the word 'batch', which distinguishes it from the singular get_market_context. It also provides default values and environment variable fallback for the API key. However, it does not explicitly state when to use this tool versus alternatives or mention any exclusions, so it lacks explicit 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 for behavioral disclosure. It states the information returned and notes that omitting api_key uses the environment key, which adds some transparency. However, it does not mention side effects, rate limits, authentication requirements, or error behavior beyond a simple fetch.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus a one-line Args section, front-loading the key purpose and then adding parameter detail. Every sentence earns its place with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool complexity, an output schema that describes return values, and a single optional parameter clearly explained, the description provides all necessary information. The combination of purpose, parameter semantics, and output schema makes this complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema only defines 'api_key' as optional string/null with no description. The tool description's 'Args' section explicitly explains the parameter's meaning, including its optionality and fallback behavior. This fully compensates for the 0% schema description coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Fetch' and clearly identifies the resource ('current Agent API key') and enumerates the returned details (plan type, active status, credits, symbol limits). This fully distinguishes it from sibling tools focused on market data or option chains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying it fetches agent API key info, but it does not explicitly state when to use this versus alternatives or provide any 'when not to use' guidance. Clear context is present, but explicit exclusions or named alternatives are missing.
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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