Pipeworx Catalog
Server Details
Pipeworx Catalog MCP — Exposes the full Pipeworx platform to Claude
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-pipeworx-catalog
- 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 4/5 across 11 of 11 tools scored. Lowest: 3.4/5.
Several tools have overlapping purposes. 'search_packs', 'discover_tools', and 'search_mcp_directory' all search for tools/packs but with subtle differences in scope. 'remember', 'recall', and 'forget' are clearly distinct for memory operations. Overall, some pairs could confuse an agent.
Naming follows a consistent verb_noun pattern (e.g., 'list_packs', 'search_packs', 'get_connection_config', 'remember', 'recall', 'forget'). Most names are clear and descriptive. Minor inconsistency: 'ask_pipeworx' uses a verb_product format, and 'discover_tools' and 'search_mcp_directory' are similar but not exactly the same pattern.
With 11 tools, the set is well-scoped for a catalog server that provides discovery, search, memory, and platform status. Each tool serves a distinct purpose, and the count is appropriate for the breadth of functionality offered.
The tools cover core operations: catalog discovery (list_packs, search_packs, discover_tools, search_mcp_directory), tool introspection (get_pack_tools, get_connection_config), platform status (get_platform_status), and memory (remember, recall, forget). A minor gap is that there is no direct way to execute a tool from the catalog besides 'ask_pipeworx', but that tool is designed to handle execution. Overall, the surface is nearly complete.
Available Tools
11 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?
With no annotations provided, the description partially discloses behavior: it auto-selects data sources and fills arguments. However, it does not specify what data sources are available, privacy implications, or what happens if the tool cannot answer (e.g., error handling).
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 very concise (4 sentences) and front-loaded with the main purpose. Every sentence adds value, including examples. No wasted words.
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 simplicity (1 parameter, no output schema) and no annotations, the description is largely complete. It covers purpose, usage, and examples. Could benefit from mentioning potential limitations or error cases, but overall 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?
Schema coverage is 100% with one parameter, and the description adds value by explaining the parameter's purpose (natural language question) and providing examples. However, it does not add constraints or format details beyond the schema.
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 takes a plain English question and returns an answer from the best data source. It explicitly differentiates from sibling tools by emphasizing natural language querying, contrasting with tools like discover_tools or get_platform_status that serve different purposes.
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 on when to use this tool (for natural language questions) and gives examples. It implicitly suggests not to use other tools for such queries, but lacks explicit exclusions or alternatives.
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 full burden. It reveals that the tool searches by natural language and returns tool names and descriptions. It doesn't mention limitations or edge cases, but the purpose is well-scoped and the behavior is clear.
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 extremely concise: three sentences that cover purpose, return value, and when to use it. No wasted words, and the most important information is front-loaded.
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 simplicity (2 params, no output schema, no nested objects), the description is nearly complete. It could optionally mention that results are ranked by relevance, but the current text is sufficient 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining that the 'query' parameter should be a natural language description with examples. This goes beyond the schema's generic description. The 'limit' parameter is also well-documented in the schema.
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 the Pipeworx tool catalog by describing what you need. Returns the most relevant tools with names and descriptions.' It specifies the action (search), the resource (tool catalog), and the result (relevant tools with names and descriptions). This distinguishes it from siblings like search_mcp_directory or list_packs, which target different catalogs or actions.
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 tells when to use the tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear guidance on usage context and priority relative to other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetAInspect
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 provided, so description carries full burden. It confirms delete operation but lacks details on irreversibility, confirmation, or error handling (e.g., what if key doesn't exist).
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?
Single, clear sentence with no extraneous words. Essential information front-loaded.
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 simple 1-parameter tool with no output schema, the description is adequate but could mention side effects (e.g., permanent deletion) and error scenarios.
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 coverage is 100%, and description does not add beyond 'key' parameter meaning. Baseline 3 is appropriate since schema already describes parameter.
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?
Description clearly states the action (Delete), resource (stored memory), and identifier (by key). Distinct from siblings like 'recall' (retrieve) and 'remember' (store).
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?
Explicitly says to delete a memory by key, implying use when removal is needed. No alternative tool for deletion exists among siblings, so no exclusion needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_connection_configAInspect
Get MCP setup instructions for connecting to Pipeworx packs. Returns connection details and gateway URLs. Use to configure your environment.
| Name | Required | Description | Default |
|---|---|---|---|
| slugs | Yes | Comma-separated pack slugs (e.g., "weather,github,jokes") or "all" for everything |
Tool Definition Quality
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 correctly indicates that the operation is a read/get (safe, non-destructive) and mentions the output formats. However, it does not disclose any side effects, rate limits, or authentication requirements, which are typical for config retrieval. The lack of behavioral depth beyond what's in the schema is acceptable but not exceptional.
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-loading the main purpose and then listing outputs. No wasted words; each sentence adds value.
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 simplicity (one parameter, no output schema, no annotations), the description is sufficiently complete. It covers what the tool does, the input format, and the output types. Minor gap: does not specify if the config includes authentication tokens or requires prior setup, but for a config retrieval tool this is acceptable.
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 coverage is 100% with a single parameter 'slugs' already described well in the schema. The description does not add additional meaning beyond the schema, but it correctly implies that 'all' is a special value. Baseline 3 is appropriate since the schema already 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 what the tool does: it retrieves MCP client config JSON for connecting to packs. It specifies the outputs (Claude Desktop config, Claude Code CLI command, gateway URL), making the purpose highly specific and distinguishable from siblings like 'get_pack_tools' or 'list_packs'.
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 when to use the tool (when you need connection config for packs) but provides no guidance on when not to use it or alternatives. Given sibling tools like 'get_pack_tools' or 'search_packs', the description could have differentiated use cases, but it does not.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_pack_toolsAInspect
Get tool definitions for a specific pack (e.g., 'weather', 'stocks'). Returns tool names, descriptions, parameters, and requirements. Use before calling a tool to verify its interface.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Pack slug (e.g., weather, pokemon, github) |
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 the tool returns tool definitions, but does not mention any side effects, authorization needs, or rate limits. Since the tool is read-only and non-destructive, a score of 3 is reasonable.
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 concise sentences, front-loaded with purpose and followed by usage guidance. No wasted words.
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 has a simple input schema (one required string parameter) and no output schema, the description is complete enough for an agent to understand its purpose and when to use it. It lacks details on output format but since no output schema exists, that is acceptable.
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 coverage is 100%, so the schema already documents the single parameter 'slug'. The description adds minimal extra meaning, merely restating that slug is a pack identifier. Baseline 3 is appropriate.
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 retrieves full tool definitions for a specific pack, listing what is included (names, descriptions, parameters). It distinguishes itself from sibling tools like discover_tools or list_packs by specifying it returns tool definitions for a particular pack.
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 advises to use this tool before calling a tool to understand its interface, providing clear usage context. However, it does not mention when not to use it or suggest alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_platform_statusAInspect
Check Pipeworx platform health and availability. Returns pack count, active tool count, and any service alerts. Use to verify system status before operations.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are empty, so description must cover behavior. It states the tool is non-destructive (reading health info) and provides a clear scope. No contradictions, but could mention that it requires no input and returns a snapshot.
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?
Single sentence, no wasted words, front-loaded with verb and resource. Perfectly concise for a simple read operation.
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?
Tool has no params, no output schema, and is a simple health check. Description is sufficient for the agent to invoke correctly. Could specify return format, but not essential.
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?
Input schema has no parameters, so schema coverage is 100%. Description adds no param info beyond the schema, which is fine because there are none. Baseline 3 is appropriate.
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?
Description clearly states the tool returns platform health information, listing specific data points (live packs, outages, degraded services, tool count). Verb 'get' plus resource 'platform status' is precise and distinct from sibling tools like 'get_pack_tools' or 'list_packs'.
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?
No explicit when-to-use or alternatives mentioned, but the tool's purpose is self-explanatory as a status check. Context signals show no parameters, making it straightforward. Sibling tools exist for more specific queries, but guidance is not strictly necessary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_packsAInspect
Browse all available Pipeworx packs. Returns pack names, categories, tool counts, and gateway URLs. Use to discover data sources or explore what's available.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Filter by category (e.g., Science, Finance, Games, Humor, Entertainment, Reference) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It discloses the output includes slug, name, category, tool count, and gateway URL, and implies a read-only listing operation. However, it does not mention pagination, rate limits, or whether the list is exhaustive or limited.
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 with no extraneous words. It front-loads the key action and result, then adds context. Every sentence is meaningful and earns its place.
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 simple input schema (1 optional parameter), no output schema, and no annotations, the description adequately covers purpose, parameters, and return fields. It could mention if the result is paginated or if there are any restrictions, but for a listing tool, this is nearly 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?
Schema coverage is 100% with a single optional parameter 'category'. The description adds value by providing example categories and clarifying that the tool lists 'all available' packs, implying the category filter is optional. This goes beyond the schema's minimal 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 verb 'list' and the resource 'all available Pipeworx MCP packs', and specifies the fields returned (slug, name, category, tool count, gateway URL). It distinguishes this tool from siblings by positioning it as the 'master inventory' and describing usage for discovery by category.
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 gives a clear context ('master inventory') and a usage suggestion ('find packs by category or discover what data sources are available'), but does not explicitly state when not to use this tool or mention alternatives among siblings (e.g., search_packs or get_pack_tools).
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 full burden. It explains the dual behavior (retrieve by key vs. list all) and that memories persist across sessions. However, it does not disclose edge cases like missing key behavior or maximum number of memories.
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, no fluff, front-loaded with the primary action. Every sentence adds value.
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 a simple tool with one optional parameter and no output schema, the description is nearly complete. It explains the two modes and persistence. Could mention error handling or performance, but not critical.
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 coverage is 100%, so the description adds minimal value beyond the schema. The description mentions 'omit to list all keys' which aligns with the optional key parameter, but doesn't add new semantics.
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 it retrieves a memory by key or lists all memories. It specifies the resource ('stored memory') and the action ('retrieve'/'list'), and differentiates from sibling tools like 'remember' (store) and 'forget' (delete).
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 says to use this tool to retrieve context saved earlier, and implies that omitting the key lists all memories. It does not explicitly exclude use cases or mention alternatives, but the sibling tools hint at distinct roles (e.g., 'remember' for storage, 'forget' for deletion).
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?
With no annotations provided, the description carries full burden. It discloses persistence differences (authenticated vs anonymous) and session duration (24 hours). This adds valuable 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each serving a clear purpose: what it does, when to use it, and persistence details. No wasted words.
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 simple key-value store with no output schema, the description is complete enough. It covers purpose, usage, and behavioral details. However, it could mention that stored values can be retrieved with 'recall' 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 describes both parameters. The description adds context about the value being any text, but this is already implied by the schema type. No significant new semantics added.
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 stores a key-value pair in session memory, with specific use cases like saving findings, preferences, and context. It distinguishes itself from siblings like 'recall' and 'forget' by focusing on storage.
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 explains when to use the tool (save intermediate findings, user preferences, context across tool calls) but does not explicitly mention when not to use it or provide alternatives. However, the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_mcp_directoryBInspect
Search thousands of MCP servers by use case (e.g., 'database', 'email', 'calendar'). Returns community and hosted servers. Use to find tools beyond Pipeworx.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10) | |
| query | Yes | Search query | |
| category | No | Filter by category |
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. It indicates the tool searches a large index but does not mention behavioral traits like result sorting, pagination, or whether it accesses external services. The description is adequate but leaves gaps for a search 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 concise with two sentences. It front-loads the key differentiator (full directory vs. hosted packs) and ends with the use case. No wasted words.
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 no output schema, the description could mention return format (e.g., server names, descriptions). It lacks info on sorting, pagination, or what happens with no results. For a simple search tool, it is mostly complete but could add return structure details.
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 describes parameters. The description does not add extra meaning beyond what the schema provides (e.g., 'query' is search query, 'category' filters, 'limit' max results). Thus baseline 3 is appropriate.
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 searches a directory of MCP servers, distinguishing it from 'search_packs' which likely searches within a subset. It specifies the scope ('full Pipeworx MCP directory' vs. 'hosted packs') and the purpose ('find MCP servers for specific use cases').
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 context (finding MCP servers) but does not explicitly state when to use this tool versus siblings like 'search_packs' or 'ask_pipeworx'. It mentions the broader scope but lacks guidance on trade-offs or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_packsAInspect
Search packs by keyword across names, descriptions, and tools (e.g., 'weather', 'translate'). Returns matching packs with details. Use to find specific capabilities.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains what fields are searched (pack names, descriptions, tool names), which is useful behavioral detail. Since no annotations are provided, the description carries the full burden; it could mention if results are ranked or paginated, but the given info is adequate.
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?
Two sentences covering purpose and usage, with example keywords. Efficient and front-loaded. No extraneous content.
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 simple search tool with one parameter, the description is mostly complete. It could mention whether the search is case-sensitive or if partial matches are supported, but the current info is sufficient for basic use.
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 'query' parameter. The description adds context that the query searches across specific fields, which adds value but is not extensive. Baseline 3 is appropriate.
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: searching Pipeworx packs by keyword. It specifies the search scope (names, descriptions, tool names), distinguishing it from sibling tools like 'list_packs' or 'discover_tools'.
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 a clear usage context: use when looking for a specific capability, with examples. However, it does not explicitly mention when not to use this tool or how it differs from 'discover_tools' or 'search_mcp_directory', leaving some ambiguity.
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
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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
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