saferagenticai-mcp
Server Details
Read-only tools over the Safer Agentic AI framework: 238 patterns + 14 heuristics.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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 4.1/5 across 12 of 12 tools scored. Lowest: 2.9/5.
Each tool has a clearly distinct purpose: finding patterns, retrieving details, listing suites/requirements/heuristics, resolving IDs, searching, and reviewing. No two tools overlap in functionality—even find_patterns_for_task and search_patterns differ in input type and output grouping.
All tool names follow the snake_case verb_noun pattern (e.g., find_patterns_for_task, get_cross_references, list_suites). The conventions are uniform—verbs are action-oriented and nouns clearly indicate the object of the action.
With 12 tools, the server covers essential operations for exploring and querying the SaferAgenticAI framework without being overwhelming. Each tool earns its place, covering discovery, retrieval, statistics, and navigation.
The tool set provides comprehensive coverage for a knowledge base: listing all suites, requirements, heuristics; searching by task or keywords; retrieving details with cross-references; resolving identifiers; and reviewing unreviewed content. No obvious gaps exist for the stated purpose.
Available Tools
12 toolsfind_patterns_for_taskAInspect
Given a natural-language task description (e.g., 'I'm building a tool-using agent that runs shell commands'), return the most relevant patterns grouped by suite. Use this as a starting point for any cross-cutting design question; then follow up with get_requirement on specific pattern_ids. Defaults to verbosity='compact' (cheap triage); pass 'full' to inline snippets and confidence flags.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | ||
| limit | No | ||
| verbosity | No | compact |
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 patterns grouped by suite, defaults to compact verbosity, and that 'full' inlines snippets and confidence flags. It is non-destructive but does not mention response structure, error handling, or what happens with no matches.
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 function, followed by usage guidance and parameter details. Every sentence provides essential information without waste.
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 and no annotations, the description covers the key aspects: input, usage flow, and verbosity options. It lacks details on the exact response format (e.g., how patterns are identified) and does not explain the limit parameter. Overall adequate for a simple search 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 coverage is 0%, so the description must compensate. It explains the task parameter (natural-language description), verbosity (compact vs full with behavior implications), but does not explain the limit parameter beyond its default. Two out of three parameters are well-described.
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 takes a natural-language task description and returns the most relevant patterns grouped by suite. It explicitly distinguishes itself from sibling tools like get_requirement and search_patterns by specifying its role as a starting point for cross-cutting design questions.
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 strong usage guidance: use as a starting point for any cross-cutting design question, then follow up with get_requirement on specific pattern_ids. It also explains verbosity options: compact for cheap triage, full for inlined snippets and confidence flags.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_cross_referencesAInspect
Return outgoing adjacencies for a pattern. explicit_cross_references are author-asserted (each pattern's cross_references YAML field). inferred_adjacent (when include_inferred=true) currently returns same-suite siblings only — it does not do semantic similarity. Treat inferred entries as 'neighbours worth scanning,' not as endorsed dependencies.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| include_inferred | No |
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 discloses that inferred entries are limited to same-suite siblings and are not endorsed dependencies. This reveals important behavioral traits, though it does not cover authorization, rate limits, or response format, which would enhance transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with only two sentences that front-load the main verb and resource. Every sentence earns its place, providing both purpose and nuance without unnecessary details.
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 covers usage and behavioral nuance well but does not explain the return format or structure of the outgoing adjacencies. It is complete enough for basic use but could benefit from return value 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 coverage is 0%, so the description compensates by explaining the 'include_inferred' parameter's effect on returning same-suite siblings. The 'id' parameter is implied as a pattern identifier but not explicitly documented. Overall, the description adds significant meaning beyond the raw schema, though slightly lacking on 'id'.
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 returns outgoing adjacencies for a pattern, distinguishing between explicit cross-references and inferred adjacent entries. It specifies that inferred entries are same-suite siblings only, not semantic similarities, which differentiates it from sibling tools like get_reverse_references.
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 guidance on when to use each mode (explicit vs. inferred) and advises treating inferred entries as 'neighbours worth scanning.' However, it does not explicitly contrast with sibling tools or state when to avoid this tool, leaving some ambiguity for an AI agent deciding between tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_operational_heuristicAInspect
Retrieve a single operational heuristic by id (e.g., 'OH::geoffrey-pattern'). Returns the full entry: principle, framework mapping, evidence sources from production deployment, design patterns, anti-patterns, and discovery narrative.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description should disclose behavioral traits. It fails to explicitly state that the tool is read-only or has no side effects. While 'retrieve' hints at a read operation, it does not confirm safety or mention any required authorizations or rate limits.
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 states the action, parameter example, and return fields concisely. Every word adds value with no 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 simplicity (single parameter, no output schema), the description adequately covers the purpose and return structure. It lists key returned fields, though it could mention error handling or non-existence cases.
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 has 0% parameter description coverage, so the description must add meaning. It explains the parameter's purpose ('by id'), provides an example format ('OH::geoffrey-pattern'), and implies the id is a string, supplementing the schema's constraints.
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 ('Retrieve a single operational heuristic') and the resource ('by id'), with an example id format. It lists the returned fields, distinguishing it from sibling tools like 'list_operational_heuristics' which retrieves multiple.
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 when a specific heuristic is needed, but does not explicitly state when not to use it or mention alternatives. The sibling tools suggest alternatives (e.g., for listing), but no direct guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_requirementAInspect
Retrieve one subgoal (framework normative content + Pattern layer guidance) by pattern_id (e.g., 'D3::idx2::sandboxing') or display_id (e.g., 'D3.2'). display_id may resolve to multiple subgoals — underlined variants share display_ids.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| include_pattern | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses that display_id may resolve to multiple subgoals and that underlined variants share display_ids, providing important behavioral context. However, it does not mention error handling or authentication needs.
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, front-loaded sentence that conveys key information without excess words. The use of parentheses and a dash reduces readability slightly but remains efficient.
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?
The description covers the main functionality and an edge case but lacks details on output structure, error responses, or behavior when the id is not found. Given no output schema and low complexity, it is adequate but not comprehensive.
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 description clarifies the 'id' parameter can be pattern_id or display_id with examples, adding significant value beyond the schema (0% coverage). The 'include_pattern' parameter is not explained, but its default and type are 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?
Description specifies the tool retrieves a subgoal with specific content types (normative + Pattern layer) by two distinct ID formats, distinguishing it from sibling tools like 'search_patterns' or 'list_requirements'.
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 usage is implied through the description and sibling tool names ('find_patterns_for_task', 'list_requirements'), but no explicit guidance on when to use this versus alternatives or what the prerequisites are.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_reverse_referencesAInspect
Return patterns that reference the given pattern_id in their cross_references. Complement to get_cross_references (outgoing); this shows incoming. Use to find all consumers of a given pattern.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
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 discloses it is a read operation showing incoming references, but omits details like pagination, auth needs, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences only, each adds distinct value, front-loaded with action. 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 low complexity (one param, no output schema), description covers core function and relation to sibling. Lacks details about return structure but sufficient for typical 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 only has 'id' with maxLength; description adds that it is a 'pattern_id', clarifying the parameter's semantic meaning 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 states the verb 'Return', the resource 'patterns', and clearly distinguishes from sibling 'get_cross_references' by specifying 'incoming vs outgoing'.
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 positions as complement to get_cross_references and states use case 'find all consumers'. Lacks when-not-to-use but provides clear alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_operational_heuristicsAInspect
List operational heuristics distilled from production agentic AI deployment (Claude Code, Rewind). These are cross-cutting safety principles discovered through building and operating AI agents, mapped to framework suites. Optional filters: suite_id (heuristics relevant to a specific suite), query (keyword search across titles and principles). Separate from the normative pattern layer — different category of knowledge.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Keyword search across titles, principles, narratives | |
| suite_id | No | Filter by framework suite (e.g., 'D3', 'I2') |
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 describes the source (production deployments) and the type (safety principles), and mentions optional filters. It implies read-only behavior without destructive effects, which is adequate for a list operation.
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, front-loaded with the main purpose. Every sentence adds value: first sentence states what and context, second sentence mentions filters and distinction. 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 and no annotations, the description covers purpose, source, filters, and distinction from pattern layer. It could mention pagination or sorting, but for a simple listing tool with optional filters, it is sufficiently 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 description coverage is 100%, so baseline is 3. The description adds value by specifying that 'suite_id' filters by heuristics relevant to a specific suite, and 'query' searches across titles and principles, which goes beyond the schema's own descriptions.
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 lists operational heuristics from production agentic AI deployments, with optional filters. It explicitly distinguishes itself from the 'normative pattern layer', differentiating from sibling tools like 'list_requirements' or 'search_patterns'.
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?
Description provides clear context on what the tool does and its filters, and mentions it is a separate category from the pattern layer. While it doesn't explicitly state when not to use it or give direct alternatives, the context is sufficient for an agent to understand its usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_requirementsCInspect
List subgoals matching filters (suite_id, suite_type, content_type, min_confidence, missing_pattern_only). Results capped by limit (default 50, max 100).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| suite_id | No | ||
| suite_type | No | ||
| content_type | No | ||
| min_confidence | No | ||
| include_pattern | No | ||
| missing_pattern_only | No |
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 burden. It mentions result capping (default 50, max 100) and filter parameters, but does not disclose mutability, side effects, or any other behavioral nuances.
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 short and front-loaded with the main action and filter list. It is efficient, though the list of filters could be more readable.
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 7 parameters, no output schema, and no annotations, the description is insufficient. It lacks return format, pagination details, ordering, and relationship to sibling tools.
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 0%, so the description must compensate. It mentions five of seven parameters (omitting include_pattern and limit), but provides no additional meaning beyond their names or types.
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 ('List') and resource ('subgoals') and lists the main filter parameters. However, it does not explicitly differentiate from sibling tools like search_patterns or list_unreviewed, which also filter and list entities.
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 guidance is provided on when to use this tool versus alternatives. It does not specify prerequisites, exclusions, or context for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_suitesAInspect
List all 16 suites in the SaferAgenticAI framework (9 drivers + 7 inhibitors) with subgoal counts and titles. Call this first to orient.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It describes a read-only list operation, specifying output includes subgoal counts and titles. No destructive behavior or auth needs are expected for a simple list.
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, front-loaded sentence with no wasted words. It efficiently conveys purpose, scope, and usage guidance.
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 without output schema, the description fully covers what is returned (subgoal counts and titles) and orients the agent. Sibling tools suggest this is a starting point, aligning with completeness.
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 zero parameters, so the description does not need to explain them. Baseline for 0 parameters is 4.
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 lists all 16 suites in the framework, with breakdown into 9 drivers and 7 inhibitors, and specifies what is returned (subgoal counts and titles). It distinguishes from siblings by being an overview tool.
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 'Call this first to orient,' providing clear context for when to use. It does not mention when not to use, but the positive guidance is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_unreviewedAInspect
Return patterns that have not been human-reviewed yet (no reviewed_by). Sorted low-confidence first, then needs_human_review flagged, then alpha. Use during Phase 3 review to pick the next pattern to examine.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses the filter (no reviewed_by) and sorting order. It does not mention any destructive or mutating behavior, which is appropriate for a read-only list operation.
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, front-loaded with purpose and key details. Every sentence adds value without 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?
The description covers purpose, sorting, and usage context. It lacks details about the limit parameter or output format, but for a simple list tool, it's largely 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 description coverage is 0%, yet the description does not explain the 'limit' parameter at all. It only describes the returned data and sorting, leaving the parameter's role undocumented.
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 returns patterns not yet human-reviewed, specifies the sorting order, and distinguishes it from sibling tools by indicating usage in Phase 3 review to pick the next pattern.
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 'Use during Phase 3 review to pick the next pattern to examine,' providing clear context. While it doesn't explicitly exclude other uses, it's sufficiently specific.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resolve_idAInspect
Resolve a loose reference (partial id, display_id, slug fragment, or title keyword) to canonical pattern_id(s). Call this when you have a rough reference and need the exact id before calling get_requirement. Always returns candidates — never 'not found'.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behaviors: it resolves references, always returns candidates (never 'not found'), and is intended as a prerequisite to get_requirement. It does not mention authorization or side effects, but the tool appears to be a read-only lookup.
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 three sentences: purpose, when to use, and a behavioral guarantee. It is front-loaded and every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose and usage well but lacks details about the output format (e.g., what 'candidates' look like). For a tool with no output schema and no annotations, more information on return structure would improve completeness.
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 description explains the 'query' parameter well (accepts partial id, display_id, slug fragment, title keyword), but does not mention the 'limit' parameter (integer with default 5). Schema coverage is 0%, so the description should cover both parameters.
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 resolves loose references (partial id, display_id, slug fragment, title keyword) to canonical pattern_id(s). It distinguishes itself from sibling tool 'get_requirement' by explicitly mentioning it should be called before that tool.
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: 'Call this when you have a rough reference and need the exact id before calling get_requirement.' It also notes that it always returns candidates. While it doesn't explicitly list when not to use it, the context is sufficiently clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
review_statsAInspect
Coverage stats: total patterns, reviewed %, per-suite and per-confidence breakdown. Surfaces load-time validation issue count.
| 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, the description carries full burden for behavioral disclosure. It mentions 'load-time validation issue count' suggesting some computation, but omits whether the tool is read-only, has performance implications, or refreshes data. This leaves the agent unsure about safety and side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with front-loaded purpose and immediate detail. Every word earns its place; no fluff or repetition.
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?
The description covers the main output facets adequately given no output schema. It could mention whether data is aggregated or live, but overall provides sufficient context for a stateless summary 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?
The input schema has zero parameters with 100% coverage, so the description adds value by detailing the specific stats returned (total patterns, reviewed %, per-suite/confidence breakdown, issue count), which the schema alone does not convey.
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 specifies the tool provides 'total patterns, reviewed %, per-suite and per-confidence breakdown' and 'load-time validation issue count', clearly distinguishing it from sibling tools like list_suites or list_requirements which focus on listing rather than stats.
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 use for high-level coverage statistics, but provides no explicit guidance on when to prefer this tool over alternatives such as find_patterns_for_task or list_unreviewed, nor any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_patternsAInspect
Field-weighted keyword search across the framework. Substring match on lowercased terms; field weights: title 10x, summary 4x, SFR text 3x, description 2x, pattern body 1x. matched_in reports the highest-weighted field that matched. No semantic / embedding search — known limitation, see /mcp.html. Use verbosity='compact' to drop snippets and confidence flags (~70% smaller payload) when triaging.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| verbosity | No | full |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses the matching algorithm (substring, lowercased), field weights, the meaning of matched_in, and the effect of verbosity on output. It also mentions a known limitation, enhancing transparency.
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 concise sentences, no filler. The first sentence clearly states purpose, followed by technical details and a practical tip. 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 3 parameters, no output schema, and no annotations, the description covers the core functionality, algorithm, limitations, and a usage optimization tip. It feels complete for a search 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 coverage is 0%, so description must compensate. It adds significant value by explaining the verbosity parameter's effect on payload size and mentioning the query's role in keyword search. However, it does not explicitly detail the limit parameter, though its schema bounds are self-explanatory.
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 it is a field-weighted keyword search, specifies substring matching on lowercased terms, and lists field weights. It distinguishes from semantic search and references a sibling limitation, making the purpose very specific.
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 when to use (keyword search) and when not (no semantic search), provides a documentation reference for known limitations, and offers a tip for using verbosity='compact' to reduce payload size when triaging.
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.
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