PropLine

Official

by proplineapi

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Remote

Server Quality Checklist

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Latest release: v0.2.3
Disambiguation5/5
Each tool targets a distinct aspect of the sports betting data lifecycle: listing sports, events, and markets; retrieving odds, EV, results, stats, historical movements, player history, scores, and resolution summaries. No two tools have overlapping purposes, and descriptions clearly differentiate them.
Naming Consistency5/5
All tools follow a consistent propline_verb_noun pattern, using 'list' for enumeration endpoints and 'get' for data retrieval. The naming is predictable and makes the action and resource easy to identify.
Tool Count5/5
With 11 tools, the server is well-scoped for a sports betting data provider. Each tool serves a clear purpose, covering discovery, odds, EV analysis, historical data, results, and stats without unnecessary bloat or gaps.
Completeness5/5
The tool surface covers the full domain: listing sports, events, markets; retrieving live odds, EV, historical movements, graded results, player history, raw stats, and a coverage summary. Essential operations are present, and no obvious dead ends exist.
Average 3.9/5 across 11 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 34 commits in the last 12 weeks
- Last stable release on May 19, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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?
The description discloses tier-based data redaction for free tier and mentions return fields. However, it does not address rate limits, idempotency, or other potential side effects, leaving gaps in behavioral understanding.
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 sentences with no fluff. Front-loaded with key purpose, then adds tier detail. Efficient and well-structured.
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?
With 4 parameters, no output schema, and no annotations, the description explains return fields and tier behavior but lacks detail on parameter interactions and output format. Adequate but not comprehensive.
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 75%. The description adds minimal value beyond the schema by summarizing returned data but does not clarify how parameters like 'limit' or 'markets' affect output.
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 it returns player prop history with specific fields like line, prices, resolution, and actual value. It distinguishes tiers but does not explicitly differentiate from sibling tools, though the name implies uniqueness.
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 on when to use this tool versus alternative propline tools. The only context is a tier difference (pro vs free), but no prerequisites or usage scenarios 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?
No annotations are provided, so the description must fully disclose behavioral traits. It states the result is 'book-agnostic' and 'decoupled from any sportsbook's lines', and notes 'Free tier', but does not mention authentication needs, rate limits, or what happens if the event is not completed. The description is too sparse given the lack of annotations.
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 composed of two concise sentences with no filler. The first sentence defines the core purpose, and the second adds relevant examples. Every word contributes value.
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 tool has only two required parameters and no output schema or nested objects, the description provides a baseline understanding. However, it lacks details on return format, error conditions, or pagination. It is minimally complete for a simple tool but leaves gaps in behavioral context.
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 input schema has two parameters (sport_key, event_id) with no description coverage (0%). The description does not elaborate on these parameters or their expected formats/values. While the description provides examples of returned stats, it adds no meaning to the parameters themselves.
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 returns 'book-agnostic raw box-score stats for a completed event', specifying the verb (implicitly 'get'), resource (raw box-score stats), and scope (per-player stats decoupled from sportsbook lines). It distinguishes from siblings like propline_get_event_results or propline_get_scores by emphasizing it provides per-player stats such as strikeouts, hits, points, etc.
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 mentions the tool is for 'completed event' and is on the 'Free tier', implying it should be used for finalized events. However, it does not explicitly state when not to use this tool or provide comparisons to sibling tools (e.g., propline_get_event_results). Usage context is implied but lacks exclusions or alternatives.
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?
With no annotations, the description carries full burden. It discloses the methodology (Pinnacle sharp line, vig removal, fair line derivation), sorting behavior, and exclusion of PrizePicks. This provides sufficient behavioral context for an agent.
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 at four sentences, well-structured with front-loading of key purpose. Every sentence adds value with no redundancy.
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 description explains core behavior but lacks details on output format, pagination, or error handling. Given no output schema, the agent lacks complete context for interpreting results.
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 25% (only min_ev_pct described). While the description adds context about computing EV per book and outcome, it does not clarify the format or usage of the other parameters (sport_key, event_id, markets). It partially compensates but not fully.
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 it returns cross-book +EV per outcome for an event, with specific methodology. It does not explicitly distinguish from sibling tools like propline_get_odds, but the tool name and description make the purpose clear.
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 prerequisites, context, or exclusions beyond being a 'Pro-tier endpoint'.
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 full burden. It only states the action (list) and gives examples, but fails to disclose whether the operation is read-only, what happens with invalid inputs, or any side effects.
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 two sentences: the first clearly states the purpose with examples, and the second adds a usage context. No unnecessary words.
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 lack of output schema and annotations, the description covers the basic purpose and a usage scenario. However, it does not specify the return format or error handling, which would be helpful for an agent.
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 input schema has zero description coverage, and the tool description does not explain the meaning or format of the required parameters (sport_key, event_id). The examples in the description are for the output, not the 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 it lists market types for a specific event with concrete examples (h2h, spreads, etc.), and the verb 'list' combined with 'market types' distinguishes it from sibling tools that handle odds, results, or stats.
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 includes a usage hint ('Useful when you don't know which prop markets...'), implying when to use it. However, it does not explicitly mention when not to use it or provide alternatives among the siblings.
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 explains the data returned (snapshot series) and tier differences, but lacks details on rate limits, authentication, or error handling. Some behavioral info is present 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?
Two concise sentences, front-loaded with purpose and supplemented with tier context. No wasted words.
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 three undocumented parameters and no output schema, the description lacks detail on parameter usage, return format, pagination, or limits. Incomplete for correct invocation.
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 has 0% description coverage and the tool description does not explain any of the three parameters (sport_key, event_id, markets). Only implicit understanding from context. Description fails to compensate.
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?
Description clearly states it returns historical line-movement snapshots for an event, distinguishing it from sibling tools like propline_get_odds (current odds).
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?
Mentions it's a Pro-tier endpoint and describes free tier behavior, giving context on when to use. No explicit exclusions or alternatives, but the tier distinction provides 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 provided, the description carries the burden. It discloses it's a free-tier endpoint and returns only aggregated counts, but lacks details on rate limits, authentication, error conditions, or data freshness. This leaves some behavioral gaps.
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 three well-structured sentences, front-loading the key fact 'Free-tier endpoint' and clearly listing output components. No redundant information.
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 simple schema and no output schema, the description reasonably covers the tool's function by listing output elements. It could elaborate on the response format but is adequate for most use cases.
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 the 'days' parameter fully described (type, range, default). The description's mention of 'over the last N days' adds no new information beyond the schema, meeting the baseline for high schema 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 clearly identifies the tool as returning aggregated counts of graded props, listing specific metrics like total graded/settled, games, sports per sport, and top-market breakdowns. It distinguishes itself from profitability claims and aligns with the tool name 'resolution_summary'.
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 explicitly states the use case: answering 'how much graded prop data does PropLine have, what's the coverage', and clarifies it is not for profitability. While it does not mention when not to use it or alternatives, the guidance is sufficient for typical scenarios.
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, so description carries full burden. Discloses Free-tier nature (suggests rate limits) and returned data types. However, does not specify how recent data is, max results, or if any side effects exist. Adequate but not rich.
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 plus a usage quote. Front-loaded with main purpose. No unnecessary words. Excellent structure.
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 simplicity (2 params, no output schema), the description covers purpose and usage adequately. Could mention pagination or limits, but not essential for typical use. Slightly incomplete regarding 'recent' definition.
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 50% (only days_from documented). Description adds context that tool returns 'recent and live' scores, but does not elaborate on sport_key format or usage beyond the schema. Adequate but not exceptional.
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?
Clearly states it returns recent and live game scores plus status for a sport. Verb 'returns' and resource 'scores and status' are specific. Distinguished from siblings like 'propline_get_event_results' by focusing on live scores.
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?
Provides a concrete use case ('is this game over yet, what was the final score') that helps agent select tool. However, no explicit when-not-to-use or alternatives given, but context is clear enough.
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 provided, so description carries full burden. It discloses pro-tier endpoint behavior, free tier redaction of resolution and actual_value, and upgrade pointer. It implies read-only operation but does not explicitly state nondestructive nature or authentication requirements.
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?
Three sentences with front-loaded purpose, followed by distinctive feature and free tier note. Every sentence adds value without redundancy. Highly 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 no output schema, the description explains return structure (graded outcomes with marks and actual values) and free tier behavior. It is sufficient for a low-complexity tool with 2 params, though pagination and error handling are not mentioned.
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 has 2 parameters with 0% description coverage, and the tool description adds no detail about sport_key or event_id beyond their names. The purpose indirectly explains them but does not provide formats, examples, or constraints.
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 it returns graded prop outcomes for completed events, specifying details like won/lost/push/void marks and actual stat values. It distinguishes itself from the-odds-api which does not grade props, making the purpose highly specific and differentiated.
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 indicates use for completed events and contrasts with the-odds-api, providing clear context. However, it does not explicitly suggest when to use this tool over other propline siblings (e.g., propline_get_event_ev), though the purpose is distinct enough.
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?
With no annotations, description discloses that it returns event IDs and team names, and behavior of 'live' parameter is implied. Does not mention read-only nature or potential errors, but adequate for a list 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?
Two sentences, no fluff, front-loaded with purpose and return fields, then usage hint.
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?
Completeness is good for a simple list tool: explains inputs, outputs, and next steps. Missing details like ordering or pagination, but not critical.
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 covers sport_key and live with 100% description coverage; description only reiterates return fields, adding no extra parameter insight.
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?
Description clearly states it lists upcoming events for a sport and specifies return fields (id, home_team, away_team, commence_time). It distinguishes from siblings like propline_get_odds or propline_get_event_results by focusing on event listing.
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?
Description implicitly guides usage by noting to use event_id for per-event details, but lacks explicit when-to-use vs alternatives or when-not-to-use.
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 full burden. It discloses that the response includes a bookmakers[] array with up to 13 books, that coverage varies by sport, and how parameters affect behavior. It lacks details on authentication or rate limits, but the core behavior is well covered.
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 (5 sentences), front-loaded with the core action, and each sentence adds necessary detail without redundancy. It is well-structured and easy to parse.
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 tool with 4 parameters and no output schema, the description adequately covers the main behaviors and response structure. It could mention error handling or supported sports, but the essentials are present.
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?
Schema coverage is 75%, and the description adds value beyond schema descriptions by providing examples (e.g., 'h2h,spreads,totals' for markets) and explaining default behaviors for event_id and markets. The parameters are well explained in combination.
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 'Get live odds' and distinguishes two modes based on event_id: full per-event props when event_id supplied, bulk game-line odds otherwise. It is specific and actionable, differentiating from siblings by naming the resource (odds) and behavior.
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 clear guidance on when to use the tool with or without event_id, and how to specify markets and bookmakers. It does not explicitly contrast with sibling tools, but the context is sufficient for common use cases.
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 provided, so description carries full burden. It explains the return format (keys, titles, active status) and that it lists currently polled sports, which is sufficient for a simple read-only 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?
Two concise sentences with no wasted words. The key information is front-loaded.
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?
The description fully covers the tool's purpose and usage given zero parameters, no output schema, and low complexity.
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?
No parameters exist; baseline score of 4 applies. The description adds no parameter-specific information, which is acceptable given the lack of 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 it lists all sports PropLine polls and specifies the output includes sport keys, human titles, and active status. This distinguishes it from sibling tools which focus on events, odds, etc.
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 advises to use this tool first to discover valid sport_key values for other tools, providing clear guidance on when and why to use it.
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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