GLM-4.7 MCP Server

Server Quality Checklist

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Latest release: v2.0.0
Disambiguation4/5
Most tools have clearly distinct roles (e.g., summarize vs. explain vs. implement), but a few pairs are close: glm_review and glm_find_bugs both analyze code, and glm_explain could be seen as a specialized glm_ask. Overall, the boundaries are clear enough that an agent will usually pick the right tool, with only minor potential for confusion.
Naming Consistency4/5
All tools share the 'glm_' prefix and use snake_case, and most follow a verb or verb_noun pattern (e.g., glm_summarize, glm_implement, glm_write_tests). The one notable deviation is glm_status, which uses a noun instead of an action, slightly breaking the pattern. Otherwise, the naming is consistent and readable.
Tool Count5/5
With 13 tools, the set is well-scoped for a GLM-powered coding assistant. Each tool serves a distinct function—question answering, summarization, explanation, analysis, review, bug finding, implementation, refactoring, test generation, documentation, README generation, status checking, and cost comparison—and none feel redundant.
Completeness4/5
The tool surface covers the full development lifecycle: from asking questions and explaining concepts to analyzing, implementing, refactoring, testing, and documenting code. Minor gaps include a dedicated 'fix' tool (though implement/refactor can handle it) and a generic conversation tool (though glm_ask covers it). No critical workflows are missing.
Average 4/5 across 13 of 13 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How to sync the server with GitHub?

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

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

How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose side effects and safety. It states 'Add or update' implying mutation of the target file, but does not clarify whether existing documentation is overwritten, whether the operation is reversible, or what permissions are needed. It also fails to specify whether the file is modified in-place or a new file is returned.
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 concise and well-structured with a one-line summary, a 'Use for' list, an Args section, and a Returns line. Each element adds value, and there is no redundant wording. It is suitable for quick scanning by an agent.
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 tool has an output schema, so return values are covered there, but the description's 'Returns: File with added documentation' is vague. Given that this is a file-modifying tool, the description is missing important context about overwrite behavior, backup, or side effects. However, the core purpose and parameters are sufficiently described for a basic understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameter descriptions (0% coverage), so the description's Args section is the only source of semantics. It provides concise definitions for all four parameters, including allowed values for style and model, which goes beyond the schema. It could be richer (e.g., explaining style differences), but it adequately compensates for the schema gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with the verb phrase 'Add or update documentation' and specifies target resources (file, docstrings, README, APIs). It is distinguishable from siblings like glm_generate_readme in scope, though not explicitly contrasted.
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 scenarios ('Use for: adding docstrings, generating README, documenting APIs'), which helps the agent decide when to select this tool. However, it does not mention alternative tools (e.g., glm_generate_readme for README-only) or exclusion criteria, so it only partially fulfills the guidance requirement.
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 disclose behavior. It lists inputs and returns but does not clarify whether the tool writes the README.md to the working directory or simply returns generated content. This is a critical ambiguity given the working_directory parameter and the potential for side effects. The description doesn't cover permissions, reversibility, or error 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 concise and well-structured: a clear intro, a use-case line, a compact args list, and a returns line. Every sentence and detail earns its place with no redundancy. The formatting improves scannability.
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 purpose, use case, parameters, and return value. However, it omits behavioral details such as whether it writes files, and doesn't address edge cases like missing working_directory or error conditions. Given that an output schema exists, the return description is useful but not sufficient to offset the missing operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It does so by naming each parameter and providing allowed values for style ('standard', 'comprehensive', 'minimal') and model ('haiku' or 'sonnet'). This adds meaningful information beyond the schema, though it doesn't explain what each style entails.
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 states a specific verb-resource pairing: 'Generate a README.md for a project using GLM.' This clearly distinguishes it from siblings like glm_summarize or glm_document, though it doesn't explicitly name them as alternatives. The combination of verb and specific resource makes the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'Use for: creating project documentation, onboarding.' line provides a clear context in which to use this tool. It does not give explicit exclusions or name alternative tools for other documentation needs, but the stated use case is adequate.
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 itself. It only mentions the return value ('Refactored code and explanation') but does not state whether the tool modifies the file in place, requires special permissions, or behaves in a read-only manner. This ambiguity is a significant gap for a refactoring tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is tightly structured with a clear purpose statement, 'Use for' list, 'Args' block, and 'Returns' line. Every sentence earns its place, with no filler or redundancy. It is concise and easy to scan.
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?
The description covers the high-level purpose, parameters, and return, but it omits critical context about side effects (e.g., does it write to the file?) and any constraints or prerequisites. Since there are no annotations and no detailed output schema guidance, this incompleteness leaves the agent guessing about the tool's operational behavior.
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 schema has no descriptions (0% coverage), so the description's 'Args' section is the sole source of parameter semantics. It provides one-line clarifications for each parameter (e.g., 'Path to file to refactor') and lists model choices, which adds some meaning beyond the bare property names. However, the explanations are shallow and mostly repeat the parameter names, so they are adequate but not rich.
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 opens with a specific verb+resource: 'Refactor code using GLM,' and immediately follows with concrete use cases ('improving code structure, applying patterns, cleanup'). This clearly distinguishes it from sibling tools like glm_ask, glm_summarize, and glm_implement, which have different purposes.
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 'Use for:' line explicitly states the target scenarios (improving structure, applying patterns, cleanup), giving the agent clear context for when to invoke this tool. However, it does not mention when not to use it or name alternative tools, so it stops short of full exclusion guidance.
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 must carry the full burden of behavioral disclosure. The word 'Check' implies a read-only operation, but the description does not explicitly state that it performs no mutations, nor does it mention required permissions or error behavior. For a status tool this is a small gap, but still undocumented.
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 and front-loaded with the primary purpose. The 'Returns' line adds a clear, useful note about the output. Every word earns its place with no redundancy or irrelevant detail.
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 zero-parameter status tool with an output schema, the description covers the essential purpose and return value. However, it lacks a suggested use case or explicit behavior guarantee (e.g., read-only), so it is not a perfect 5. The presence of an output schema compensates for not explaining return values in detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the input schema is empty with 100% coverage. The description adds no parameter-specific information, but none is needed. With no parameters to document, the baseline of 4 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 uses the specific verb 'Check' with a clear resource ('GLM MCP server status and configuration'), which unambiguously distinguishes it from all sibling tools that focus on content generation, analysis, and code operations. It is a precise verb+resource phrase.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus its 12 siblings. The name and description imply a diagnostic purpose, but there is no explicit context such as 'Use this to verify server connectivity before running other GLM tools.' The description does not mention alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior on its own. It lists inputs and says 'Returns: Generated test file,' but leaves ambiguity about whether the tool actually writes to disk or simply returns content. It also does not mention overwrite behavior, permissions, or network/API implications of using GLM.
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 tightly organized: one-sentence purpose, 'Use for' line, args list, and returns. Every line is informative with no redundant content, making it highly scannable.
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?
While an output schema exists, the description fails to disclose critical side effects for a 'write' tool—whether it creates or overwrites a file, and what working_directory actually does. This gap is significant for an agent deciding whether to invoke it and how to handle the result.
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 schema has zero description coverage, and the description compensates by listing each parameter with a short gloss and enumerating allowed values for test_framework and model. However, file_path and working_directory are vague ('Path to file to test', 'Project directory') and lack format or default behavior details.
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 opens with a specific verb+resource: 'Generate unit tests for a file using GLM.' It further clarifies scope with 'Use for: test generation, coverage improvement, TDD support,' which distinguishes it from sibling tools like glm_analyze or glm_review.
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 gives explicit use cases ('test generation, coverage improvement, TDD support'), telling the agent when to choose this tool. However, it does not mention when NOT to use it or explicitly name alternatives, so it lacks full exclusions.
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 carries the full burden of behavioral disclosure. It only states the operation and return format, but does not reveal whether it uses current pricing data, makes network calls, requires authentication, or has any side effects. This minimal information does not sufficiently disclose behavioral traits.
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 concise and well-structured, with a clear separation into purpose, arguments, and returns. It is front-loaded with the main action and contains no redundant or filler text.
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 comparison tool, the description covers the purpose, parameters, and return format. It lacks details about pricing sources or currency, but given that an output schema exists and the tool is straightforward, it provides sufficient context to understand how to invoke it. Missing usage guidance is already scored separately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no property descriptions (0% coverage), but the description adds one-line explanations for each parameter: 'Input tokens to compare' and 'Output tokens to compare.' This adds meaningful context beyond the schema's titles and defaults, clarifying the role of each token count.
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 'Compare costs between Claude and GLM' with a specific verb and resource, distinguishing it from sibling tools like glm_ask or glm_summarize. The purpose is unambiguous and action-oriented.
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?
No explicit guidance is provided on when to use this tool versus alternatives. The verb 'compare costs' implies usage for cost comparison, but the description does not mention exclusions or alternative tools, making the usage implied rather than explicit.
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 must disclose behavior. It states the return format and mentions 'using GLM' (implying an external model call), but it does not disclose side effects, data handling, permission requirements, or the fact that this is a non-destructive analysis. This leaves behavioral expectations underspecified.
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 well-structured with purpose, usage, args, and returns. It avoids filler and is easy to scan. The bullet-style sections earn their place.
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 the tool's simplicity and presence of an output schema, the description covers the core purpose, parameters, return format, and usage context. It does not cover edge cases like error behavior or file path resolution details, but these are not critical for basic invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must define parameters. It does: code_or_file, working_directory, and model are all explained, including the two model options. It could mention defaults and the optional nature of working_directory, but it goes beyond the raw 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 opens with a clear verb and resource ('Find potential bugs in code') and reinforces with specific use cases ('bug detection, edge case analysis, error prone patterns'), distinguishing it from sibling tools like glm_explain or glm_review.
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?
Provides explicit 'Use for' list that defines when to apply the tool, but does not explicitly mention when not to use it or name alternative tools. The use case list is clear enough to guide selection among siblings.
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 provided, the description carries the burden of behavioral disclosure. It does state that it returns 'Code review with findings and suggestions' and names the GLM agent, but it doesn't disclose whether code is sent externally, whether the operation is read-only, or any other side effects. Basic transparency is present but not rich.
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 structured with sections for purpose, use cases, arguments, and return value. It's compact and every line adds information, though the opening 'Code review by GLM agent' is somewhat redundant with the tool name.
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?
The description covers purpose, usage, parameters, and return value, and the presence of an output schema fills any gap about return structure. However, it doesn't mention operational caveats like network requirements or whether files are modified, which would be useful for a review tool operating alongside mutation tools like glm_refactor.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description compensates well by explaining each parameter's meaning and enumerating allowed values for review_focus and model (e.g., 'security', 'performance', 'style'; 'haiku', 'sonnet'). This goes beyond the bare schema and helps the agent select appropriate inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs code review via a GLM agent, and specifies use cases (security review, performance analysis, best practices). This distinguishes it from sibling tools like glm_ask or glm_implement.
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?
It provides explicit 'Use for' guidance, listing the types of review it supports, which gives clear context for when to invoke it. However, it doesn't mention alternatives or explicitly state when not to use it, so it falls short of fully differentiating from similar tools like glm_analyze.
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 alone must disclose behavior. It states the tool summarizes text and returns the summarized result, which covers the core behavior. It does not discuss potential side effects, rate limits, or caveats, but the operation is inherently read-only and the return value is specified.
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 compact and well-structured: a one-sentence purpose, a use-for list, an args section, and a return statement. Every line adds necessary information.
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?
Despite having no annotations and a minimal schema, the description covers the tool's purpose, typical usage contexts, all three parameters with allowed values, and the return value. This is sufficient for a straightforward summarization tool, though it doesn't address edge cases like maximum input length.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema lacks descriptions for its properties, but the description compensates by listing each argument and its purpose, including explicit allowed values for style and model. This adds practical meaning beyond the bare 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 opens with 'Summarize text using GLM,' which clearly states the verb and resource. It further lists specific use cases (document summaries, meeting notes, code explanations), distinguishing it from sibling tools like glm_explain or glm_analyze.
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 'Use for' line provides clear contexts for when to apply the tool, covering document summaries, meeting notes, and code explanations. However, it does not explicitly mention when not to use it or reference alternatives, stopping short of full comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description is the sole source of behavioral information. It explicitly declares READ-ONLY access and enumerates the available capabilities (Read, Glob, Grep, LS, Bash), which clearly communicates the tool's safety profile. It also notes 'Bash (safe commands)' to indicate restricted execution, though the exact safety criteria aren't defined.
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 organized into terse, front-loaded sections: purpose, usage, access, arguments, and returns. Every line adds value, and the structure makes it scannable for an AI agent. The 'Returns' line is brief but acceptable given the presence of an output schema.
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 read-only analysis tool with three parameters and an output schema, the description covers all key aspects: purpose, use cases, safe access boundaries, and parameter meanings. It lacks explicit examples or exclusions, but the read-only statement and use-case list provide sufficient context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The Args section provides human-readable explanations for all three parameters, compensating for the 0% schema description coverage. It clarifies that 'task' is the analysis instruction, 'working_directory' defaults to current, and 'model' accepts 'haiku' or 'sonnet.' This goes beyond the raw schema titles and types.
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 first line 'Analyze codebase using GLM with read access' clearly defines the action and target. The 'Use for' section specifies distinct analysis tasks (code structure, patterns, architecture, dependency mapping) that differentiate it from sibling tools like glm_implement or glm_refactor. The read-only designation further distinguishes it from modification tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases under 'Use for,' outlining when to employ this tool. The read-only access statement implicitly indicates it should not be used for modifications, but it doesn't name specific alternative tools for those cases, which would have pushed it to a 5.
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 provided, the description carries the transparency burden. It clarifies the return value ('Returns: Explanation') and that it's an explanation task, but doesn't disclose operational details like model cost, latency, or that the model choices ('haiku', 'sonnet') have different capabilities.
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 front-loaded with the primary purpose, followed by a bulleted usage list, an Args block with clear annotations, and a Returns line. Every sentence delivers value with no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 3-parameter explain tool with an output schema, the description covers the purpose, usage context, all parameters, and return type. It's sufficiently complete for an agent to select and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description fully compensates by giving meaningful semantics for each argument: code_or_concept, context, and model with allowed values. Without this, the agent would only have parameter names and types.
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 opens with a clear verb+resource statement: 'Explain code or a concept using GLM.' It also lists use cases (understanding code, learning concepts, documentation) that distinguish it from siblings like glm_summarize and glm_analyze.
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 'Use for' section provides clear context for when to use it (understanding, learning, documentation), but it doesn't explicitly name alternatives or when-not to use it, so it's a step below the highest bar.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the tool's mutation capability with 'Has FULL access including Write and Edit' and 'WARNING: This can modify files', and it mentions that the output includes 'changes made'. This goes beyond the name and schema, though it could add more detail on scope or reversibility.
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 compact and well-structured: a clear opening line, a 'Use for' list, a warning, a parameter list, and a returns note. No redundant sentences or filler.
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 the tool's mutating nature, the description covers the core aspects: purpose, warning, parameters, and return information. The output schema is noted, and the 'Returns' line suffices. Minor gaps like lack of detail on how changes are applied or any security/backup considerations prevent a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. The 'Args:' section explains each parameter's purpose, such as 'task: Implementation task to perform' and 'working_directory: Project directory (REQUIRED)'. While 'allowed_tools' is somewhat vague ('full coding set'), the descriptions add meaningful context for all parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'GLM agent with write access for implementation tasks' and lists concrete use cases ('writing code, creating files, making changes, refactoring'). It distinguishes from sibling tools by emphasizing 'FULL access including Write and Edit' and the warning about modifying files, which sets it apart from read-only analysis tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases under 'Use for:' and warns 'WARNING: This can modify files', signaling when caution is needed. However, it does not explicitly name alternatives or say when not to use it, though the sibling tool names imply read-only options.
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 provided, the description carries the full burden of behavioral disclosure. It discloses the lack of tool/file access, speed, cost, and return format. This is solid, though it omits details like rate limits or token caps, which are not critical for this simple tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, with a front-loaded summary followed by bullet-like usage notes. Every sentence serves a purpose, and the cost note is a useful extra without being verbose.
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?
The description covers purpose, parameters, and return value, and an output schema is present. It is complete for a simple two-parameter tool, though it could mention potential errors or rate limits, which are not essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides clear meaning for both parameters: 'question' is described as 'Your question or prompt', and 'model' is explained with allowed values and their trade-offs ('haiku' fastest, 'sonnet' better quality). The schema has no descriptions, so the description adds essential value.
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 opens with 'Quick question to GLM - no tools, fast response,' clearly stating the tool's purpose and scope. The 'Use for' list (explanations, analysis, brainstorming, quick answers) differentiates it from specialized siblings like glm_summarize and glm_analyze.
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?
It explicitly lists use cases and states that the tool 'Does NOT have access to files or tools - pure generation', which tells when not to use it. However, it does not name specific sibling tools for specific tasks, so the guidance is clear but not exhaustive.
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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