GSEP-MCP

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by gsepcore

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v1.0.9
Disambiguation5/5
Each tool has a distinct role in the security pipeline: scanning input, output, actions; middleware hooks; chat; status; and feedback. No two tools serve the same purpose; even similar-sounding tools like gsep_scan_input and gsep_before_llm are clearly differentiated by their descriptions.
Naming Consistency5/5
All tools follow the 'gsep_' prefix with a verb_noun pattern (e.g., scan_input, get_status, record_feedback) or preposition_noun (before_llm, after_tool). The naming is consistent, descriptive, and predictable.
Tool Count5/5
With 10 tools, the server is well-scoped for its domain of LLM security pipeline. Each tool addresses a specific step or integration point, neither too few nor too many.
Completeness5/5
The tool set covers the complete lifecycle: input scanning, action scanning, output scanning, middleware hooks for external agents, chat pipeline, status monitoring, and feedback. There are no obvious gaps for the intended purpose.
Average 3.7/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- Last stable release on May 11, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the key behavioral trait of blocking destructive actions using a firewall, which is beyond the empty annotations. However, it lacks details on side effects (e.g., what happens when blocked), error handling, or idempotency, leaving notable gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences, no fluff, and front-loads the purpose. Every word contributes meaning, making it efficient for quick parsing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the schema covering parameters, the description is too brief for a middleware hook with a firewall mechanism. It omits information about the C5 Action Firewall, what constitutes destructive, and how the tool's output or status affects subsequent execution. This leaves the agent with incomplete context for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers all parameters with descriptions (100% coverage). The tool description adds no parameter-level information, so it does not enhance understanding beyond the schema, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a middleware hook that runs before agent actions to block destructive ones using a firewall. It's specific about the verb (run) and resource (agent actions), and distinguishes from siblings like gsep_after_tool by focusing on pre-execution. However, it could be slightly more precise about its role as a security interceptor.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like gsep_scan_input or gsep_before_llm. It does not specify prerequisites, context, or exclusions, leaving the agent without direction on appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must fully disclose behavioral traits. It reveals that the tool runs checks, records fitness, and returns a safe_response, but fails to specify side effects (e.g., database writes, state changes), what happens if checks fail, or whether it blocks or is async. This leaves significant gaps for an agent to predict behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the core purpose, and contains no filler. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a middleware hook with no output schema, the description should cover return value format, error handling, and pipeline integration. It mentions safe_response but does not describe its structure or behavior on failure. While the core flow is clear, key operational details are missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description adds minimal new meaning. It generically names the tool's purpose but does not elaborate on how parameters like genome_id, user_message, or response are used beyond what the schema already says. Falls at baseline for high-coverage schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool is a middleware hook that runs after an external LLM responds, performing C4 behavioral immune checks, recording fitness, and returning a safe_response. This distinguishes it from sibling hooks like gsep_before_llm (runs before LLM) and gsep_after_tool (runs after tool calls), providing a specific verb and resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage 'after an external LLM responds,' but does not explicitly state when to use or avoid this tool versus alternatives like gsep_before_llm or gsep_scan_output. No guidance on prerequisites or scenarios where this should be bypassed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It lacks details about side effects, return format, auth requirements, or rate limits. It only lists detection categories without explaining what happens after scanning.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no fluff: first sentence states core purpose and number of checks, second gives concrete examples. Front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers what the tool does and what it detects, but lacks information about the output format (no output schema), error handling, or any asynchronous behavior. For a tool without output schema, more detail would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 does not add extra meaning beyond the parameter descriptions in the schema; it just restates the tool's purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool scans LLM output for behavioral infection (Indirect Prompt Injection) and lists specific checks (system prompt leakage, role confusion, data exfiltration). It distinctly differentiates from sibling tool 'gsep_scan_input' which would scan user input.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (after LLM output) but does not explicitly state when not to use it or provide alternative tool suggestions. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It lists pipeline stages (C3, enhanced LLM call, C4, C5, fitness tracking, autonomous evolution) and says it returns a protected response. However, it does not disclose potential side effects, state changes, or prerequisites beyond the API key parameter.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short (two sentences) and front-loaded with the main action. The list of steps could be better structured (e.g., numbered), but it is still concise and readable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 6 parameters, no output schema, and no annotations, the description covers the pipeline but lacks details on return format, error behavior, or parameter interactions. It is minimally adequate but leaves gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with parameter descriptions in the input schema. The description adds no parameter-specific information. Baseline of 3 is appropriate since the schema already provides meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it sends a message through the full GSEP pipeline and lists the steps (C3, enhanced LLM, etc.). This distinguishes it from siblings like gsep_scan_input or gsep_scan_output, which handle only subtasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this is the main chat endpoint but does not explicitly state when to use it versus individual scan tools (e.g., gsep_scan_input). No guidance on prerequisites or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions what the tool returns but fails to disclose behavioral traits such as side effects, error conditions, permissions required, or what happens on failure. The description is insufficient for an agent to fully understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no wasted words. The first sentence states purpose and outputs, the second gives usage guidance. Perfectly structured for a tool description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters, no output schema, and no annotations, the description is adequate but not comprehensive. It explains the tool's role as a hook and its return value, but lacks details on error handling, C3/security status meaning, or parameter interdependencies. The schema covers the parameters, so it's minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 all 5 parameters. The description adds value by listing the return values (enhanced_prompt, sanitized_message, C3/security status), but provides no additional parameter-level semantics beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool is a middleware hook executed before LLM calls, specifying it returns enhanced_prompt, sanitized_message, and C3/security status. It distinguishes from siblings by explicitly contrasting when to use this tool versus owning the LLM call.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage context: 'Use this when GSEP-MCP protects an existing external agent rather than owning the LLM call.' This implies an alternative (owning the LLM call via gsep_chat) but does not name it explicitly or exclude other scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It does not mention side effects, state changes, authentication needs, or performance. It only lists detection categories, which is helpful but insufficient for full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose and scope, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool is simple and schema covers parameters fully, but description lacks information about return value or output format. Without output schema, some guidance would be beneficial for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 context by listing detection types but does not elaborate on individual parameters beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it scans user input using C3 Content Firewall and lists specific attack types detected. It is distinct from siblings like gsep_scan_output, which likely handles output scanning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Use this before sending any external content to your LLM', providing clear when-to-use context. Does not explicitly mention when not to use or alternatives, but sibling differentiation is implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description must disclose behavior. It states that it scans for prompt injection and dangerous actions but does not explain what happens upon detection (e.g., blocks, sanitizes, passes through). This partial disclosure is adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at two sentences, front-loading the core purpose and scanning action without any extraneous information. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's purpose but lacks details on what it outputs or what happens when threats are detected. Given 6 parameters, no output schema, and no annotations, the description should provide more behavioral context to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters are described in the input schema with clear descriptions. The tool's description does not add new meaning beyond the schema, which is acceptable given 100% schema coverage. Baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the tool as a middleware hook executed after a tool returns external content and before it reaches an LLM, scanning for prompt injection and dangerous actions. This distinguishes it from siblings like gsep_before_tool (before tool) and gsep_after_llm (after LLM).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies where in the pipeline the tool belongs (after tool, before LLM) and contrasts with similarly named siblings (e.g., before_tool). However, it does not explicitly state when to use this tool or when not to, relying on the naming convention for differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses what the tool returns (health, fitness, drift, generation, security stats) and the two modes of operation (specific genome or all). However, it does not mention whether the tool is read-only, side-effect-free, or any other behavioral constraints beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences: the first lists the data fields retrieved, the second explains the parameter behavior. No redundant words; information is front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one optional parameter and no output schema, the description adequately covers the return fields and usage modes. It could be improved by clarifying the response structure (e.g., single object vs. list) or error scenarios, but it is largely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and already fully describes the single parameter, including the behavior when omitted. The tool description echoes this without adding new semantic meaning, so it meets the baseline but does not exceed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb 'Get' and the resource 'genome status' with specific fields (health, fitness scores, drift status, evolution generation, security stats). It distinguishes itself from sibling tools like gsep_scan_input or gsep_chat by focusing on status retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear guidance on when to use the optional genome_id parameter: omit to list all active genomes, include for a specific genome. It implies the tool is for inspection purposes, but does not explicitly state when not to use it or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key behavioral traits: positive signals reinforce configuration, negative signals trigger evolution. With no annotations, the description carries the full burden; though missing details like reversibility or permissions, the core side effects are explained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two front-loaded sentences: first states purpose, second explains behavioral consequences. No redundancy or unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Explains the outcome of feedback (reinforcement/evolution) which is essential context. Lacks mention of return value or success/failure behavior, but given no output schema and clear schema descriptions, it is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 no extra meaning beyond the schema for parameters like 'note', 'quality', or 'user_id'. The 'satisfied' parameter is tied to 'positive/negative signals' but that is already clear from the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool records satisfaction feedback for a genome, with specific verb 'record' and resource 'genome'. Distinguishes from sibling tools (e.g., scanning, chat) via unique purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage context (after a response, to provide feedback) and explains consequences (reinforcement or evolution), but lacks explicit when-to-use or when-not-to-use guidance compared to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description reveals key behavior: it permanently blocks dangerous actions and classifies them into categories (safe/caution/destructive/critical). However, it does not clarify whether blocking is automatic or if the tool returns results for agent decision. The permanent blocking disclosure is valuable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no extraneous information. Every sentence provides essential detail about scanning, classification, and blocking.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description covers the tool's purpose, parameters, and side effects (permanent blocking). However, it lacks details on the return format or how results are communicated, leaving some ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description adds no extra meaning beyond the schema, particularly for the 'genome_id' parameter which is not mentioned. Baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool scans LLM responses for dangerous/destructive actions using C5 Action Firewall, classifies them, and permanently blocks specific actions. It provides specific examples of blocked actions (rm -rf, DROP DATABASE) and distinguishes from sibling tools like gsep_scan_input or gsep_scan_output by focusing on actions in responses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for scanning actions in LLM responses but does not explicitly state when to use this tool versus alternatives (e.g., scanning input or output). No when-not or alternative tool guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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