poker-mcp

Overview Schema Related Servers Score Discussions

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Each tool targets a distinct aspect of poker simulation: table lifecycle, hero actions, analysis, and stats. However, submit_action and autoplay_bots both advance the hand, though their triggers differ, which could cause slight confusion.
Naming Consistency5/5
All tool names use consistent snake_case with verb_noun format. Abbreviations like calc are acceptable, and multi-word nouns are hyphen-free but clear.
Tool Count5/5
With 11 tools, the server covers table management, decision submission, analytical helpers, and data import without bloat. Every tool serves a necessary role for playing poker hands.
Completeness4/5
The tool set provides essential CRUD for tables, actions, and equity calculations. Minor gaps include no way to modify table parameters after creation or retrieve full hand histories, but core workflows are covered.
Average 3.7/5 across 9 of 11 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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
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.
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```
{
  "$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

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 declares a read operation but does not disclose authentication requirements, error handling, or side effects. Minimal transparency.
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?
Single, clear, and efficient sentence with no unnecessary words. Perfectly front-loaded.
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?
Provides the core functionality but lacks details on edge cases, prerequisites (table existence), and output structure. However, the presence of an output schema partially mitigates the need to describe return values.
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% and the description adds no meaning for the 'table_id' parameter beyond its name. The tool name and parameter name imply its purpose, but the description should compensate for the missing schema documentation.
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?
Description clearly states the tool returns the full current state summary for a table. It distinguishes from siblings like 'list_tables' or 'get_pending_actions' by focusing on a comprehensive snapshot. However, it could be more specific about what the state includes.
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 alternatives like 'get_pending_actions' or 'advise'. The description only states what it does, not the context or prerequisites.
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?
Annotations are absent, so the description must disclose all behavioral traits. It mentions the return value (boolean for existence), but omits critical details like whether the action is reversible, if it requires special permissions, or impacts table state for other users. This is acceptable for a simple operation but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that efficiently conveys the core action and return type. It contains no unnecessary words and is 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?
Given the tool's simplicity (1 required param, presence of output schema), the description covers the basics. However, without annotations, it fails to address potential side effects or prerequisites, such as whether the table must be active or if associated data is permanently deleted. More context would improve usability.
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?
With 0% schema coverage, the description fails to add meaning beyond the schema. It merely states 'by id' which is already implied by the required parameter. No details on format, allowed values, or constraints for table_id are provided.
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?
Description clearly states the tool closes and removes a table by ID, and returns whether it existed. It distinguishes from sibling tools like list_tables, create_table, and get_table_state by focusing on removal. However, it does not elaborate on what 'remove' entails, which could be slightly ambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, nor any prerequisites or context (e.g., table must exist, effects on ongoing actions). The description is purely functional without usage 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 full burden for behavioral disclosure. It only mentions 'minimal stub' and the event structure, but does not explain mutation behavior, idempotency, validation, or error handling, leaving significant gaps.
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 short and front-loaded with the core purpose, followed by a single line listing fields. It is concise but could be slightly more structured with separation of purpose and details.
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?
Despite an output schema being present, the description lacks context on success conditions, idempotency, bulk limits, and data validation. The 'minimal stub' note suggests incompleteness, making the tool under-specified for reliable use.
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 lists field names but provides no additional meaning (e.g., valid action values, the meaning of 'voluntary'), offering minimal value beyond the schema titles.
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 'import' and the resource 'raw hand-history events', distinguishing it from sibling tools like list_tables or get_opponent_stats which operate on different data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for seeding opponent stats from external data, but does not explicitly state when to use or avoid this tool versus alternatives, leaving guidance implied rather than explicit.
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 full burden. It discloses that the tool returns aggregate stats from stored hands, but lacks details on error handling, permissions, or data freshness.
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 that efficiently communicates the tool's purpose with no extraneous 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 tool has one required parameter and an output schema (not provided), the description is minimally complete. However, it could benefit from noting what happens if player_id is invalid or missing.
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 for parameters. The description does not explain the player_id parameter beyond its name, leaving the agent to infer its meaning from context.
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 specifies the verb 'Return', the resource 'aggregate stats (VPIP/PFR/AF)', and the subject 'a player from stored hands'. It differentiates well from sibling tools like get_table_state or advise.
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 implies usage when historical player stats are needed, but does not explicitly state when to use this tool versus alternatives like advise or calc_equity. No exclusions or conditions are given.
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 provided, the description carries the full burden. It discloses the core behavior (advancing non-hero bots) and the stopping condition. However, it does not mention side effects, reversibility, or authorization needs, which would enhance transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the action and condition. Every word is functional; there is no unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and an output schema (not provided in description). The description explains behavior but omits details on parameter semantics and output. It is minimally complete for a straightforward automation tool.
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 coverage is 0% for the 'table_id' parameter, and the description does not explain its meaning or usage. The agent must infer that it identifies the table, but no additional context is provided beyond the schema definition.
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 'advance' and the resource 'non-hero bots', and specifies the termination condition ('until it is the hero's turn or the hand ends'). It distinguishes from sibling tools like 'submit_action' and 'get_pending_actions' by focusing on automated bot advancement.
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 implies usage for automating bot turns but does not explicitly state when to use this tool versus alternatives or when not to use it. The context from sibling tools helps, but explicit guidance is missing.
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 must fully disclose behavior. It states the method (Monte Carlo) and mentions opponents are random, but does not disclose potential edge cases (e.g., invalid card formats, board length constraints), error behavior, or return value details. The output schema exists but is not referenced, leaving 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 and well-structured: a one-line summary followed by a docstring-style parameter list. Every sentence provides value, and there is no redundancy or wasted words. It is front-loaded with the core purpose.
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 moderate complexity (4 parameters, Monte Carlo simulation) and the absence of annotations, the description covers the main aspects: algorithm, input format, and parameter semantics. It lacks details on output format, error handling, and performance, but the presence of an output schema mitigates the need to describe return values explicitly. Still, some completeness is lost.
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 description coverage is 0%, so the description must add meaning. It explains each parameter's role: hole cards, board cards (with allowed lengths), number of opponents, and number of trials. This goes beyond the bare type information in the schema. However, it does not mention defaults (5000, 1) which are in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: estimating hero equity via Monte Carlo simulation against random opponents. It provides a specific verb ('Estimate') and resource ('equity'), and distinguishes from sibling tools like 'advise' or 'get_opponent_stats' which serve different functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention when not to use it. The context suggests it is for equity calculation, but no comparison to siblings like 'advise' is given. This leaves some ambiguity for the agent.
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 full burden. It discloses that bots auto-advance until the hero's turn or hand ends, which is a key behavioral trait. However, it does not mention any side effects, permissions, or rate limits, leaving gaps for a creation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: a short paragraph plus a bullet-like list of arguments. Every sentence adds value, and the key purpose is front-loaded. No wasted words.
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 6 parameters, no annotations, and an output schema (which covers return values), the description is quite complete. It explains the auto-advance behavior and each parameter. However, it doesn't mention how to reference the created table (e.g., table ID), though siblings like get_table_state imply it. Still, nearly comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates fully with an Args section that explains each parameter: seats (2-9), small_blind (chips), big_blind (> small_blind), starting_stack, hero_seat (seat index), bot_profile (options like 'mixed'). Constraints and ranges are given, adding meaning beyond the schema's default values.
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 'Create a new simulated no-limit hold'em table and deal the first hand.' This specifies the action (create), resource (table), and type (no-limit hold'em). It distinguishes from siblings like list_tables (which lists existing tables) and close_table (which destroys).
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 indicate when to use this tool versus alternatives, nor does it mention prerequisites or situations where this tool should be avoided. There is no explicit guidance on context of use.
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 provided, so description carries full burden. Mentions table_id must be hero's turn, but lacks details on whether tool is read-only, side effects, or what 'mc_trials' default implies. Leaves 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?
Two clear sentences plus args list. Front-loaded purpose, no wasted words. Ideal length for a simple tool.
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?
Tool is simple with 2 params and output schema exists. Description covers key points (hero turn, mc_trials purpose). Could mention output type or that no state is changed, but output schema likely provides that.
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 0%, so description compensates well. Adds meaning: table_id requires hero's turn, mc_trials defines equity estimate rollouts. Could add constraints like range for mc_trials.
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 uses specific verb 'Recommend' and resource 'hero action', clearly stating it combines equity, pot odds, and preflop charts. This distinguishes it from siblings like 'calc_equity' (equity only) and 'submit_action' (execution).
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?
Usage is implied: call when you need a recommended action during hero's turn. No explicit when-not-to-use or alternatives mentioned. Could improve by stating not to use for executing actions or calculating raw equity.
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 full burden. It states a read operation (listing) without disclosing side effects, permissions, or rate limits. For a simple list, it is adequate but not explicit.
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?
Single sentence front-loads purpose and return value with zero waste.
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?
Tool has an output schema (not shown), so return details are covered. Description hints at 'state summaries' which is sufficient for a simple list tool. Could mention filtering but not necessary.
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 schema coverage is 100% vacuously. The description adds no parameter info, which is acceptable. Baseline for zero parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear verb 'List' and specifies resource 'open tables' and what is returned ('state summaries'). This distinguishes it from siblings like get_table_state (specific table) and create_table/close_table.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives. Usage is implied from context but lacks guidance on when not to use it or mention of 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 that after submission, bots play out until the hero's turn again or hand ends. It lists valid actions and clarifies that 'amount' is required for bet/raise, which adds important behavioral context beyond the schema.
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 extremely concise: one sentence for purpose, then an 'Args:' list. Every sentence provides necessary information without redundancy. The structure is front-loaded 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?
Given the presence of an output schema (not shown but indicated), the description need not detail return values. It covers the core functionality and the auto-advance behavior. However, it doesn't explicitly state that the tool is only valid when it's the hero's turn, which would enhance completeness.
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 description coverage is 0%, so the description must compensate. It explains 'table_id' as target table, 'action' with the list of options, and 'amount' as 'total chips to bet/raise to' clarifying semantics (not increment). This adds significant meaning beyond the schema's type definitions.
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 'Submit the hero's action, then auto-advance bots to the next decision,' which specifies both the verb and resource. It distinguishes this tool from siblings like 'autoplay_bots' (auto-plays all) and 'advise' (gives suggestions) by focusing on submitting a specific action for the hero.
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 explains the action submission and subsequent auto-advance but does not explicitly state when to use this tool versus alternatives like 'autoplay_bots' or 'get_pending_actions'. It provides context but lacks clear when-not-to-use guidelines.
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?
Description explains the polling mechanism and lifecycle, which adds behavioral context. However, it does not mention potential empty returns or latency, but given the simple nature, it is sufficient.
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 the main purpose front-loaded. Every word is relevant and efficient.
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 an output schema (implied), the description fully explains the tool's role in the workflow, making it complete for an AI agent.
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, so the baseline is 4. The description adds no parameter info but none is needed.
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 every table waiting for the hero to act, using a specific verb 'Return' and specifying the resource. It distinguishes from siblings like 'submit_action' by positioning itself as the polling driver.
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 states the polling pattern: poll, act via submit_action, then poll again. This provides clear when-to-use and suggests the sibling tool for action.
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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