gametheory-mcp

by ryuxik

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.2
Disambiguation5/5
All tools have clearly distinct purposes: auction tools handle format, bidding, reserve, and simulation; mechanism tools cover matching, optimal auctions, and posted prices; negotiation tools handle buy/sell offers and attack detection. No overlap or ambiguity.
Naming Consistency5/5
Tools follow a strict 'gt_domain_action' pattern (e.g., gt_auction_optimal_bid, gt_negotiation_sell_next_offer). Naming is descriptive and uniform across all three domains.
Tool Count5/5
10 tools is well-proportioned for the server's scope: 4 auction, 3 mechanism, 3 negotiation. Each tool serves a specific, non-redundant function.
Completeness4/5
Covers core game theory mechanisms (auctions, matching, posted price, negotiation). Minor gaps exist, such as absence of common bargaining protocols or other auction formats (e.g., Dutch auction).
Average 2.9/5 across 10 of 10 tools scored. Lowest: 2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
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.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 for behavioral disclosure. It does not state whether the operation is read-only (computation without side effects), what assumptions are made (e.g., independent private values), or any limitations. The agent cannot infer safety or side effects from the description alone.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, unclear sentence. While brevity can be beneficial, here it sacrifices clarity entirely. It does not front-load key information (e.g., the tool's action) and reads more like a cryptic note than a helpful description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has three parameters (one complex), no output schema, and no annotations, the description fails to provide essential context. There is no mention of return value format, expected behavior, or how to interpret the result. The agent has insufficient information to use the tool reliably.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero documentation coverage, and the description adds no meaningful explanation for any of the three parameters. 'bidder_value_prior' is a nested object with additionalProperties true, but the description does not clarify its expected structure (e.g., distribution type, parameter format). 'seller_valuation' is hinted at by the description's phrasing, but not explicitly. This leaves the agent with no guidance on how to populate the parameters correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Myerson optimal reserve from virtual-value-equal-seller-valuation' implies the tool computes an optimal reserve price using Myerson's theory, but it lacks an explicit verb (e.g., 'computes' or 'calculates') and relies heavily on domain-specific terminology. It distinguishes from siblings like 'gt_auction_optimal_bid' or 'gt_auction_simulate' only by naming the specific concept, but a general-purpose agent may not grasp the purpose clearly.
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 such as 'gt_mechanism_optimal_auction_design' or 'gt_auction_format_recommendation'. There is no mention of prerequisites, context, or when-not-to-use, leaving the agent without decision criteria for 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?
With no annotations, the description must disclose behavior but only mentions a static price and DP schedule. It does not explain underlying assumptions (e.g., buyer arrival model), side effects, or whether the tool is deterministic or stochastic despite parameters like n_simulations and seed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it lacks structure and fails to front-load key information. It is not overly verbose, but brevity does not compensate for missing details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, no annotations, and a complex nested parameter. The description does not specify what the tool returns or how to interpret results, making it severely incomplete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, and the tool description does not explain any parameter. The object parameter 'buyer_arrival_prior' is opaque, and optional parameters like 'n_simulations' and 'seed' are not clarified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names the specific algorithm (Gallego-van Ryzin) and mentions posted-price with static price and dynamic schedule, indicating it computes optimal posted prices. However, it does not explicitly state the tool's purpose in a simple verb+resource form, and it assumes familiarity with the algorithm.
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 siblings like gt_auction_optimal_bid or gt_mechanism_gale_shapley. The description lacks context for when posted-price is appropriate.
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 should disclose behavioral traits such as output format, randomness control, or side effects. It only states 'Monte Carlo auction revenue + efficiency', omitting details like return structure or reproducibility (seed parameter is present but not mentioned).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (only 4 words), but its brevity sacrifices necessary information. It is not structured with sentences or bullet points, but it avoids verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters, 3 required, and no output schema, the description is severely incomplete. It fails to clarify return values, parameter usage, or expected results, making it inadequate for tool invocation.
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 does not explain any parameters. Critical parameters like 'bidder_priors' (array of objects with arbitrary properties) remain undefined, leaving the agent unable to construct valid input.
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 'Monte Carlo auction revenue + efficiency' indicates the tool uses Monte Carlo simulation to compute revenue and efficiency metrics for auctions. This is clear enough given the tool name includes 'simulate', but it does not explicitly state the action 'simulates'.
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 like gt_auction_format_recommendation or gt_auction_optimal_bid. The description lacks context for appropriate usage scenarios.
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?
The description discloses that Vickrey is truthful and that first-price uses BNE for symmetric IPV, but omits behavioral details for English ascending, assumptions (e.g., independent private values), computational complexity, or any limitations. With no annotations, the description should provide richer behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise (two sentences) and front-loaded with purpose, but it is too brief relative to tool complexity. Some important information is omitted, which reduces efficiency for the agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 6 parameters (4 required), a nested object (competitor_value_prior), no output schema, and no annotations, the description fails to cover return values, assumptions, edge cases, or how parameters interact. It is severely incomplete for a complex auction optimization tool.
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%, so the description must explain parameter meaning. It only mentions auction_format implicitly and does not clarify critical parameters like competitor_value_prior (how to structure), risk_aversion (role), or reserve_price. The word 'optimal bid' hints at valuation and competitors but is insufficient for correct use.
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 computing optimal bids for three auction formats (first-price, second-price Vickrey, English ascending). It uses a specific verb ('optimal bid') and resource ('auction formats'), distinguishing it from siblings like format recommendation or reserve price optimization.
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 on when to use this tool versus alternatives (e.g., gt_auction_simulate, gt_mechanism_*). The description implies use when computing an optimal bid given valuations, but lacks context about prerequisites or situations where other tools are more appropriate.
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 carry the full burden. It mentions theoretical behavior (e.g., per-bidder reserves, collapses to second-price) but omits practical aspects like simulation, randomness, or side effects. The presence of n_simulations and seed parameters hints at simulation, but this is not disclosed in the description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise, consisting of only two sentences. While it avoids verbosity, it sacrifices completeness, especially regarding parameter usage. It is front-loaded with purpose but lacks structure for easy consumption.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the parameter count (5) and lack of output schema and annotations, the description is insufficiently complete. It does not explain inputs, outputs, or practical usage, leaving significant gaps for an agent to correctly invoke the tool.
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%, requiring the description to compensate. However, the description does not mention any of the 5 parameters (bidder_priors, seller_valuation, objective, n_simulations, seed), providing no additional meaning beyond the schema's minimal types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool implements the Myerson revenue-optimal mechanism for asymmetric IPV. It distinguishes itself from siblings by specifying the mechanism type and the condition under which it collapses to a simpler format, making the purpose evident.
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 asymmetric IPV settings and notes the special case of symmetric IPV, but does not explicitly state when to use alternatives or provide criteria for tool selection. Some guidance is present, but it is incomplete.
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, and the description does not disclose behavioral traits such as return type, side effects (likely none), or requirements. It only states the output format enumeration without further detail on behavior beyond the recommendation.
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: a single sentence that is front-loaded with the core action and outcome. Every word is necessary, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (4 parameters including a nested object, no output schema, no annotations), the description is severely incomplete. It fails to explain critical input parameters, the nature of the recommendation (single vs. ranked), or any constraints, leaving the agent with insufficient information.
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?
With 0% schema description coverage and only 'weights' mentioned in the description, two other required parameters (bidder_value_prior, n_bidders, seller_valuation) are left entirely unexplained. The description adds minimal meaning beyond what the input schema provides.
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: 'Recommend format from {first_price, vickrey, english} given weights.' It specifies the verb 'recommend', the resource 'format', and the allowed values, distinguishing it from sibling tools like gt_auction_optimal_bid or gt_auction_simulate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. The phrase 'given weights' hints at a condition but does not explain the role of weights or provide context for appropriate use, nor does it mention when not to use it.
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 must carry the full burden. It discloses that the tool returns a recommended offer plus warnings and defense actions, and describes a conditional behavior. However, it does not clarify whether the tool has side effects (e.g., modifying state) or requires special permissions, and it doesn't explicitly state that it is read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, concise and front-loaded with the primary purpose. It avoids unnecessary details. A minor improvement would be to separate the return values more clearly, but it is appropriately sized and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, no output schema, no annotations), the description is insufficient. It provides high-level purpose and one conditional, but does not cover critical context such as the algorithm behind the recommendation, the role of each input, or the format of the return values beyond listing them. The agent lacks enough information to use the tool confidently.
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 explain parameters. Only one parameter (market_prior) is partially described with a condition. The other six parameters (my_reservation, seller_offer_history, etc.) receive no explanation beyond their names in the schema. This leaves agents guessing their semantics and formats.
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 is a 'Buy-side next-offer recommendation', which conveys the verb (recommend) and resource (next offer). This distinguishes it from the sibling tool 'gt_negotiation_sell_next_offer'. However, it could be more explicit about what 'recommendation' entails (e.g., an offer price).
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 mentions a conditional scenario involving 'anchor_attack_detection' and market_prior, but it provides no explicit guidance on when to use this tool versus alternatives (e.g., when to choose buy-side vs sell-side, or when to use the detection tool first). No 'when not to use' or comparison to siblings is given.
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. It reveals the mathematical operation (z-score) and the three possible recommendations, but does not explain the derivation, side effects, or error conditions. The transparency is moderate but lacks detail.
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 very concise, consisting of two short sentences that capture the core functionality without 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite conciseness, the description lacks completeness. It does not explain the return format, the expected structure of market_prior (keys mu and sigma), or how the z-score recommendation is determined. Given the complexity and lack of output schema, more detail is needed.
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 clarifies market_prior as {mu, sigma}, but there is a mismatch: the opponent_offer_history is described as 'opponent's opening offer' (singular) while the schema expects an array of numbers (history). This inconsistency reduces clarity.
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: to detect an anchor attack by z-scoring the opponent's opening offer against a market prior, and to recommend an action. This differentiates it from sibling tools which are about auctions and mechanism design.
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 you have an opponent's opening offer and a market prior, but it does not explicitly state when to use this tool versus alternatives like gt_negotiation_buy_next_offer or gt_negotiation_sell_next_offer. No when-not conditions are provided.
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 partially fulfills the transparency burden by disclosing the pareto knob interpolation and the return values (recommended offer, acceptance probability, expected payoff, posterior). However, it does not mention idempotency, side effects, or required authorization.
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: two sentences with no extraneous words. Every sentence adds value, covering the tool's core purpose, a key parameter, and return values.
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 complexity (6 parameters, no output schema, sibling tools), the description adequately explains the tool's function and outputs but lacks parameter definitions for the required inputs and does not describe the output structure in detail.
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%, requiring the description to compensate. Only the 'pareto_knob' parameter is explained (range and effect). The other five parameters, including all four required ones, receive no additional semantic context beyond their names.
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 a sell-side next-offer recommendation, distinguishing it from the sibling 'gt_negotiation_buy_next_offer'. It specifies the verb 'recommend', the resource 'next offer', and the side 'sell', making its 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage in sell-side negotiation scenarios but does not explicitly state when to use this tool versus alternatives like auction tools or the buy-side counterpart. No exclusions or conditional guidance are provided.
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 disclosure burden. It explains the algorithm type and return value (proposer-optimal, sanity check). It does not explicitly state side effects (likely none) or authorization needs, but the description is transparent enough for a computation 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 two sentences, very concise, and front-loaded with the key algorithm name. Every sentence adds value, with no wasted words.
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 no output schema, the description explains the return value. The two parameters are adequately described with required fields, and the scope of the tool is fully captured for an agent to use it correctly.
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 coverage is 0%, but the description compensates by specifying that proposers and receivers must have 'id' and 'preferences' fields, and receivers optionally a 'capacity' field. This adds critical meaning beyond the generic 'object' 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 performs stable matching via deferred acceptance (Gale-Shapley). It specifies inputs (proposers and receivers with preferences) and output (proposer-optimal matching and blocking-pair check), distinguishing it from sibling auction and negotiation tools.
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 implicitly indicates usage for stable matching with proposer-optimal outcome. It does not explicitly exclude alternatives, but the context of sibling tools (auctions, negotiations) makes the domain clear.
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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