gamedeals
Server Details
Gamedeals MCP — wraps CheapShark API (game deal aggregator, no auth required)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-gamedeals
- GitHub Stars
- 0
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.8/5 across 8 of 8 tools scored. Lowest: 2.9/5.
Most tools have distinct purposes, but there is some overlap between search_deals and search_games, as both involve searching for games, which could cause confusion. However, their descriptions clarify that search_deals focuses on deals with filters, while search_games is for title-based searches, mitigating ambiguity.
The naming follows a consistent snake_case pattern with clear verb_noun structures (e.g., get_game_details, list_stores, search_deals). Minor deviations exist with discover_tools and forget, which are less descriptive, but overall the naming is predictable and readable.
With 8 tools, the count is well-scoped for a game deals server, covering core functionalities like searching, listing stores, and memory management. Each tool serves a specific purpose without feeling excessive or insufficient for the domain.
The toolset covers key aspects of game deals, including searching, store listing, and price details, with memory tools for context. A minor gap is the lack of tools for user-specific features like wishlists or alerts, but core workflows are adequately supported.
Available Tools
9 toolsask_pipeworxAInspect
Ask a question in plain English and get an answer from the best available data source. Pipeworx picks the right tool, fills the arguments, and returns the result. No need to browse tools or learn schemas — just describe what you need. Examples: "What is the US trade deficit with China?", "Look up adverse events for ozempic", "Get Apple's latest 10-K filing".
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Your question or request in natural language |
Tool Definition Quality
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 Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which explains the automation behavior. However, it lacks details on limitations (e.g., data source availability, error handling, or rate limits), leaving gaps in behavioral context for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core functionality, followed by practical guidance and examples. Every sentence earns its place: the first explains the purpose, the second details the mechanism, the third provides usage guidance, and the examples clarify scope. It is appropriately sized with zero wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (natural language query processing) and lack of annotations or output schema, the description does well by explaining the automation behavior and providing examples. However, it could be more complete by mentioning potential limitations or the types of data sources covered, as the output format and error cases are unspecified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'question' well-documented in the schema. The description adds value by emphasizing 'plain English' and 'natural language,' and provides concrete examples (e.g., 'What is the US trade deficit with China?') that illustrate the expected input format beyond the schema's basic description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'), distinguishing it from sibling tools like search_games or list_stores which are more specific. The examples further clarify the scope of questions it handles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' This implies it should be used for natural language queries instead of manually selecting specialized tools, providing clear guidance on its intended context versus alternatives like discover_tools or search_deals.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discover_toolsAInspect
Search the Pipeworx tool catalog by describing what you need. Returns the most relevant tools with names and descriptions. Call this FIRST when you have 500+ tools available and need to find the right ones for your task.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of tools to return (default 20, max 50) | |
| query | Yes | Natural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries") |
Tool Definition Quality
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 key behavioral traits: it's a search operation that returns relevant tools, and it should be called first in certain contexts. However, it doesn't mention potential limitations like rate limits, authentication requirements, or error conditions that would be important for a discovery tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise and well-structured in two sentences. The first sentence explains what the tool does, and the second provides critical usage guidance. Every word earns its place with zero waste or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search operation with 2 parameters) and lack of annotations/output schema, the description provides good contextual coverage. It explains the purpose, when to use it, and the general behavior. However, it doesn't describe the return format or potential search limitations that would be helpful for a discovery tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any additional parameter semantics beyond what's in the schema (e.g., it doesn't explain query formatting nuances or limit implications). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('search', 'returns') and resources ('Pipeworx tool catalog', 'most relevant tools with names and descriptions'). It distinguishes itself from sibling tools by focusing on tool discovery rather than game/store/deal operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context about when to use this tool versus alternatives, including the threshold condition (500+ tools) and the primary use case (finding tools for a task).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetCInspect
Delete a stored memory by key.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key to delete |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Delete' implies a destructive mutation, but doesn't specify whether this action is reversible, requires permissions, has side effects, or what happens on success/failure. For a destructive tool with zero annotation coverage, this is a significant gap in 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's destructive nature (implied by 'Delete'), no annotations, and no output schema, the description is incomplete. It lacks critical information about behavioral traits, error handling, and output expectations, which are essential for safe and effective tool invocation in this context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'key' documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Delete') and the resource ('a stored memory by key'), which is specific and unambiguous. However, it doesn't explicitly differentiate this tool from sibling tools like 'recall' or 'remember', which appear related to memory operations, so it doesn't fully achieve sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 doesn't mention prerequisites, exclusions, or refer to sibling tools like 'recall' (which likely retrieves memories) or 'remember' (which likely stores them), leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_game_detailsAInspect
Get complete pricing history for a game: current deals across all stores, historical low prices, and price trends over time.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | CheapShark game ID (obtained from search_games) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what data is returned (price details, history, deals) but does not cover important traits such as rate limits, authentication needs, error handling, or whether this is a read-only operation. The description adds value by specifying the scope of data but misses key behavioral aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the main action ('Get full price details') and lists specific data points clearly, making it easy to understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is adequate but has gaps. It explains what data is returned but does not address behavioral aspects like rate limits or error handling. Without annotations or output schema, the description should provide more context on how the tool behaves, but it partially compensates by detailing the data scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'id' parameter documented as 'CheapShark game ID (obtained from search_games)'. The description does not add any additional meaning beyond what the schema provides, as it does not mention parameters at all. Baseline 3 is appropriate since the schema handles parameter documentation effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get full price details') and resources ('for a game'), distinguishing it from siblings like 'search_games' (which finds games) and 'search_deals' (which finds deals). It explicitly lists the types of details returned: price history, cheapest price ever, and current deals across stores.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying that the ID should be 'obtained from search_games' in the schema, but it does not explicitly state when to use this tool versus alternatives like 'search_deals' for deals or 'list_stores' for store information. It provides some context but lacks explicit guidance on exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_storesAInspect
View all tracked game retailers (e.g., Steam, Epic Games, GOG). Returns store names and IDs for filtering deals by specific stores.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a list operation that returns data, implying it's read-only and non-destructive. However, it doesn't mention potential limitations like rate limits, authentication requirements, or whether the list is cached/real-time.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences that each earn their place: the first states the action and resource, the second explains the return value and its purpose. No wasted words, front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter list tool with no annotations and no output schema, the description provides adequate context about what it does and why. It could be more complete by mentioning the format of the returned data or any behavioral constraints, but it covers the essential purpose and usage linkage well.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the schema already fully documents the absence of parameters. The description appropriately doesn't add parameter information beyond what the schema provides, maintaining focus on the tool's purpose and output.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List all game stores') and resource ('tracked by CheapShark'), distinguishing it from siblings like search_deals or search_games. It explicitly identifies what gets returned ('store names and IDs') and their purpose ('for use with search_deals').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('for use with search_deals'), establishing its role as a prerequisite for another sibling tool. However, it doesn't explicitly state when NOT to use it or mention alternatives like get_game_details for different purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recallAInspect
Retrieve a previously stored memory by key, or list all stored memories (omit key). Use this to retrieve context you saved earlier in the session or in previous sessions.
| Name | Required | Description | Default |
|---|---|---|---|
| key | No | Memory key to retrieve (omit to list all keys) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the dual functionality (retrieve by key or list all) and persistence across sessions, which is valuable. However, it doesn't mention error handling (what happens if key doesn't exist), format of returned memories, or any rate limits/constraints. The description adds some behavioral context but leaves gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences that each earn their place. The first sentence explains the core functionality with conditional logic, and the second provides usage context. No wasted words, and information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (dual functionality, session persistence), no annotations, and no output schema, the description does a good job covering the essentials. It explains what the tool does, when to use it, and parameter semantics. The main gap is lack of information about return format/output structure, which would be helpful since there's no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single parameter. The description adds meaningful context by explaining the semantic effect of omitting the key ('omit to list all keys') and connecting the parameter to the tool's dual functionality. This goes beyond what the schema provides about the parameter's purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings by mentioning 'context you saved earlier' which differentiates it from tools like 'search_games' or 'discover_tools' that don't involve stored memories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('to retrieve context you saved earlier in the session or in previous sessions') and includes conditional usage instructions ('omit key to list all keys'). It also implicitly distinguishes from alternatives like 'remember' (for saving) and 'forget' (for deleting).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rememberAInspect
Store a key-value pair in your session memory. Use this to save intermediate findings, user preferences, or context across tool calls. Authenticated users get persistent memory; anonymous sessions last 24 hours.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key (e.g., "subject_property", "target_ticker", "user_preference") | |
| value | Yes | Value to store (any text — findings, addresses, preferences, notes) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool performs a write operation (storage), specifies persistence characteristics (authenticated users get persistent memory, anonymous sessions last 24 hours), and implies session-scoped functionality. It doesn't mention rate limits, error conditions, or specific permission requirements, but covers the essential behavioral aspects for a memory storage tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise and well-structured in just two sentences. The first sentence states the core purpose, the second provides usage guidelines and behavioral context. Every word earns its place with no redundancy or unnecessary elaboration. The information is front-loaded with the primary function stated immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with no annotations and no output schema, the description provides good contextual completeness. It explains what the tool does, when to use it, and important behavioral characteristics (persistence differences). The main gap is the lack of information about return values or confirmation of successful storage, but given the tool's relative simplicity and the clear behavioral transparency, it's reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters (key and value) with clear descriptions. The tool description doesn't add any additional parameter semantics beyond what's in the schema - it doesn't explain parameter relationships, constraints, or usage patterns. The baseline of 3 is appropriate when the schema does all the parameter documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('store a key-value pair') and resource ('in your session memory'). It distinguishes from sibling tools like 'recall' (likely for retrieval) and 'forget' (likely for deletion) by focusing on storage functionality. The description goes beyond the name/title by specifying what kind of data can be stored.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'to save intermediate findings, user preferences, or context across tool calls.' It also distinguishes usage scenarios based on authentication status (authenticated vs. anonymous sessions), giving clear context for application. No explicit alternatives are named, but the context implies when this tool is appropriate versus retrieval or deletion tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_dealsBInspect
Search active game deals with optional filters by store, platform, or discount level. Returns deal title, store, sale price, normal price, savings %, Metacritic score, and deal rating.
| Name | Required | Description | Default |
|---|---|---|---|
| title | No | Filter deals by game title (partial match supported) | |
| sort_by | No | Sort order: "Deal Rating" (default), "Price", "Metacritic", or "Reviews" | |
| store_id | No | Filter by store ID (use list_stores to get IDs) | |
| page_size | No | Number of results to return (default: 10, max: 60) | |
| lower_price | No | Minimum price filter | |
| upper_price | No | Maximum price filter (e.g., 5 for deals under $5) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return fields (deal title, store, prices, etc.) which is helpful, but doesn't describe pagination behavior (though page_size parameter hints at it), rate limits, authentication requirements, or error conditions. For a search tool with 6 parameters, this leaves significant behavioral aspects undocumented.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise - two sentences that efficiently convey the core functionality and return format. The first sentence states the purpose, the second lists return fields. Every word earns its place with zero redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 6 well-documented parameters but no annotations and no output schema, the description provides adequate but incomplete context. It covers what the tool does and what it returns, but lacks behavioral details (pagination, errors, limits) and sibling tool differentiation. The absence of an output schema means the description's return field listing is valuable, but overall completeness is just adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so all parameters are well-documented in the schema itself. The description adds minimal value beyond the schema - it mentions 'optional filters' which aligns with the schema's 0 required parameters, but doesn't provide additional context about parameter interactions or usage patterns. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search for game deals with optional filters.' It specifies the resource (game deals) and action (search). However, it doesn't explicitly differentiate from sibling tools like 'search_games' - both involve searching, but one is for deals and the other for games. The distinction is implied but not stated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. There are three sibling tools (get_game_details, list_stores, search_games), but the description doesn't mention any of them or explain when this search_deals tool is appropriate versus searching for games directly. No context about prerequisites or exclusions is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_gamesAInspect
Find games by title to compare current prices across stores. Returns cheapest price, deal ID, and availability info for price tracking.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return (default: 10) | |
| query | Yes | Game title to search for |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return data (price and deal ID) but lacks details on error handling, rate limits, authentication needs, pagination, or whether the search is case-sensitive. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and followed by return details. Every word earns its place with no redundancy or fluff, making it highly efficient and easy to parse for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search function with two parameters), no annotations, and no output schema, the description is adequate but incomplete. It covers the purpose and return data but lacks behavioral context (e.g., error cases, performance limits) and detailed output structure, which would be needed for full agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema fully documents both parameters ('query' for game title and 'limit' for result count). The description adds no additional parameter semantics beyond what the schema provides, such as search syntax or format examples, 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search for games by title') and resource ('games'), distinguishing it from sibling tools like 'get_game_details' (detailed view), 'list_stores' (store listing), and 'search_deals' (deal-focused search). It explicitly mentions the return data (cheapest price and deal ID), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for finding games by title, but it does not explicitly state when to use this tool versus alternatives like 'search_deals' or 'get_game_details'. There is no guidance on prerequisites, exclusions, or comparative contexts, leaving the agent to infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!