RenderDoc MCP Server

Overview Schema Related Servers Score Discussions

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting specific aspects of RenderDoc capture analysis, such as checking availability, opening/closing captures, retrieving lists of resources, analyzing draw calls, or fetching specific data like pixel colors or thumbnails. There is no significant overlap in functionality, making tool selection straightforward for an agent.
Naming Consistency5/5
All tool names follow a consistent 'renderdoc_verb_noun' pattern with snake_case, such as 'renderdoc_open_capture' and 'renderdoc_get_textures'. This uniformity enhances readability and predictability, allowing agents to easily infer tool purposes from their names.
Tool Count5/5
With 12 tools, the server is well-scoped for analyzing graphics captures, covering core operations like capture management, resource listing, detailed analysis, and data retrieval. Each tool serves a specific and necessary function without redundancy, making the count appropriate for the domain.
Completeness4/5
The toolset provides comprehensive coverage for capture analysis, including opening/closing captures, retrieving resource lists, analyzing draw calls and shaders, and accessing pixel data. A minor gap exists in operations like modifying captures or exporting data, but core analysis workflows are fully supported, allowing agents to perform most essential tasks.
Average 3.3/5 across 12 of 12 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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

Behavior2/5
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 states the tool performs 'detailed analysis' but doesn't specify what that entails (e.g., computational cost, output format, or side effects). This is a significant gap for a tool with potential complexity, as it lacks details on behavior beyond the basic action.
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 a single, efficient sentence that front-loads the core action and lists key components without waste. Every word contributes to understanding the tool's purpose, making it appropriately sized and well-structured.
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?
Given the complexity implied by 'detailed analysis' and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the analysis returns (e.g., a report, structured data) or behavioral traits like performance impact, leaving gaps that could hinder effective tool use by an agent.
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 has 100% description coverage, clearly documenting the 'event_id' parameter. The description adds no additional parameter semantics beyond what the schema provides, such as how to obtain valid event IDs or format requirements. This meets the baseline score of 3 since the schema handles the heavy lifting.
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 action ('perform detailed analysis') and the target ('a specific draw call'), with specific components listed ('input/output resources, shaders, and state'). It distinguishes from siblings like 'renderdoc_get_draw_calls' (which likely lists draw calls) by focusing on analysis rather than retrieval, though it doesn't explicitly name alternatives.
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. It doesn't mention prerequisites (e.g., needing an open capture), exclusions, or related tools like 'renderdoc_get_draw_calls' for selecting event IDs, leaving usage context 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 carries the full burden of behavioral disclosure. It states the action ('Get pixel color data') but lacks critical details: it doesn't specify the return format (e.g., RGBA values, error handling), performance implications (e.g., computational cost for large textures), or dependencies (e.g., whether the texture must be loaded or accessible). For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves beyond the basic operation.
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 a single, efficient sentence that front-loads the core action ('Get pixel color data') and specifies the context ('at a specific location in a texture'). There is zero waste or redundancy, making it easy to parse quickly. Every word earns its place by clearly conveying the tool's function without unnecessary elaboration.
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?
Given the complexity of a pixel data retrieval tool with no annotations and no output schema, the description is incomplete. It lacks information on return values (e.g., data format, error cases), behavioral nuances (e.g., coordinate bounds, texture state requirements), and integration with sibling tools (e.g., how it relates to 'renderdoc_get_textures'). For a tool that likely returns structured color data, more context is needed to use it effectively without trial and error.
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%, with all parameters well-documented in the input schema (e.g., 'texture_id' as resource ID, 'x' and 'y' as coordinates, 'mip' as mip level with default). The description adds no additional parameter semantics beyond implying a 'specific location' for x and y. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract from the existing documentation.
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 purpose with a specific verb ('Get') and resource ('pixel color data'), specifying the action and target. It distinguishes from siblings like 'renderdoc_get_textures' (which lists textures) or 'renderdoc_get_texture_thumbnail' (which provides images) by focusing on individual pixel data extraction. However, it doesn't explicitly contrast with all siblings, such as 'renderdoc_analyze_draw_call', which might involve pixel analysis in a different context.
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. It doesn't mention prerequisites (e.g., requiring an open capture or specific texture state), exclusions (e.g., not for buffer data), or direct comparisons to siblings like 'renderdoc_get_textures' for texture metadata or 'renderdoc_get_texture_thumbnail' for visual previews. Usage is implied only by the action described, with no explicit context or alternatives provided.
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 full burden but only states what the tool does without behavioral details. It doesn't disclose if this is a read-only operation, requires specific states (e.g., an active capture), has performance implications, or what the output format might be, leaving significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy to parse quickly.
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?
Given the complexity of shader inspection and the lack of annotations and output schema, the description is insufficient. It doesn't explain what 'detailed information' or 'reflection data' entails, potential errors, or dependencies on other tools (e.g., needing an open capture), making it incomplete for effective 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?
Schema description coverage is 100%, with the parameter 'resource_id' fully documented in the schema as 'The resource ID of the shader to inspect.' The description adds no additional meaning beyond this, such as how to obtain the resource ID or format specifics, so it meets the baseline for high schema coverage without compensating value.
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 verb 'Get' and the resource 'detailed information about a specific shader including its reflection data', making the purpose understandable. It distinguishes from siblings like 'renderdoc_get_buffers' or 'renderdoc_get_textures' by specifying shader information, though it doesn't explicitly contrast with them.
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 alternatives. The description lacks context about prerequisites (e.g., needing an open capture) or exclusions, and it doesn't reference sibling tools like 'renderdoc_search_resources' for finding shaders or 'renderdoc_get_capture_info' for broader context.
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 of behavioral disclosure. It mentions searching by name pattern but does not describe key behaviors like whether the search is case-sensitive, if it returns partial matches, the format of results, potential rate limits, or error handling. For a search tool with zero annotation coverage, this is a significant gap in 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?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core action and resources, making it easy to parse quickly, with every part contributing essential information.
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?
Given the tool's complexity as a search function with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, result format, and usage context, which are crucial for an AI agent to invoke it correctly. The high schema coverage helps with parameters, but overall completeness is inadequate for effective tool selection and 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?
Schema description coverage is 100%, so the schema already documents both parameters ('pattern' and 'resource_type') with descriptions and an enum. The description adds minimal value by mentioning 'name pattern' and resource types, but does not provide additional semantics beyond what the schema offers, such as wildcard examples or default behavior details, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search for resources') and specifies the resource types ('textures, buffers, shaders') with a search method ('by name pattern'), making the purpose evident. However, it does not explicitly differentiate from sibling tools like 'renderdoc_get_buffers' or 'renderdoc_get_textures', which might retrieve resources without searching by pattern, so it lacks sibling distinction for a perfect score.
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, such as sibling tools like 'renderdoc_get_buffers' for direct retrieval or 'renderdoc_analyze_draw_call' for different contexts. It implies usage for searching by pattern but offers no explicit when/when-not instructions or named alternatives, leaving the agent to infer 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?
With no annotations, the description carries the full burden of behavioral disclosure. It states it's a read operation ('Get'), but doesn't cover aspects like permissions, rate limits, response format, or whether it requires an active capture. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and includes essential details without unnecessary elaboration.
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's moderate complexity (list operation with one optional parameter), no annotations, and no output schema, the description is minimally adequate. It covers the purpose but lacks behavioral context and usage guidelines, leaving gaps that could hinder effective tool selection and invocation.
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 the optional 'filter' parameter. The description doesn't add any parameter-specific details beyond what's in the schema, such as examples of filter patterns or how filtering works. Baseline 3 is appropriate when the schema handles parameter documentation.
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 verb ('Get') and resource ('list of all buffers in the capture'), specifying what properties are included ('size, type, etc.'). It distinguishes from siblings like 'renderdoc_get_textures' by focusing on buffers, but doesn't explicitly contrast with other buffer-related tools (none listed).
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 on when to use this tool versus alternatives is provided. The description doesn't mention prerequisites, context (e.g., after opening a capture), or exclusions, leaving the agent to infer usage from the tool name alone.
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 the full burden of behavioral disclosure. It states it 'Get[s] a list' but doesn't clarify if this is a read-only operation, whether it requires specific permissions, or details on pagination behavior beyond the schema. For a tool with no annotations, this lacks critical context like rate limits or error conditions.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse and understand quickly.
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's moderate complexity (list retrieval with pagination), no annotations, and no output schema, the description is minimally adequate. It specifies what is retrieved but lacks details on return format, error handling, or dependencies. With no output schema, more information on the result structure 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 description coverage is 100%, so the input schema fully documents the two parameters (start_index and count) with defaults and constraints. The description adds no additional parameter semantics beyond implying a list retrieval, which the schema already covers. This meets the baseline for high schema 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 the verb 'Get' and resource 'list of all draw calls/events in the capture with their properties', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'renderdoc_analyze_draw_call' or 'renderdoc_search_resources', which might offer similar functionality with different scopes or approaches.
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. It doesn't mention prerequisites (e.g., needing an open capture), exclusions, or compare it to siblings like 'renderdoc_analyze_draw_call' for detailed analysis or 'renderdoc_search_resources' for filtered searches, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It describes what information is retrieved but lacks details on behavioral traits such as whether this is a read-only operation (implied by 'Get'), potential errors if no capture is open, performance characteristics, or output format. This leaves significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose and scope ('detailed information about the currently open capture') with specific examples ('API, driver, and resource counts'). It is front-loaded with the core action and avoids any redundant or extraneous information.
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 0 parameters, no annotations, and no output schema, the description provides basic purpose and context but is incomplete for effective use. It doesn't explain the return values or format, error conditions, or dependencies on other tools (e.g., requiring an open capture). For a tool with no structured data support, more behavioral and output details would improve completeness.
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 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics beyond the schema, and it appropriately focuses on the tool's purpose without unnecessary parameter details, earning a baseline score of 4 for zero-parameter tools.
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 purpose with a specific verb ('Get') and resource ('detailed information about the currently open capture'), including what information is retrieved (API, driver, and resource counts). However, it doesn't explicitly differentiate from sibling tools like 'renderdoc_get_buffers' or 'renderdoc_get_textures' that also retrieve specific types of capture information, so it falls short of a perfect score.
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 context by specifying 'currently open capture', suggesting this tool should be used after a capture is opened (e.g., via 'renderdoc_open_capture'). However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'renderdoc_get_buffers' for buffer-specific data or 'renderdoc_get_draw_calls' for draw call details, nor does it mention prerequisites 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?
No annotations are provided, so the description carries full burden. It mentions what properties are returned but doesn't disclose behavioral traits like whether this is a read-only operation, if it requires an open capture, performance characteristics, or error conditions. The description is functional but lacks important operational context.
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 a single, well-structured sentence that efficiently conveys the tool's purpose and scope. Every word earns its place with no redundancy or unnecessary elaboration.
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 single-parameter read operation with no output schema, the description adequately explains what the tool returns but lacks context about prerequisites (e.g., requires an open capture), return format details, or limitations. It's minimally viable but leaves gaps in operational understanding.
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 the optional 'filter' parameter. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter semantics.
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 verb ('Get a list') and resource ('all textures in the capture') with specific properties mentioned (dimensions, format, mip levels). It distinguishes from sibling tools like renderdoc_get_buffers (for buffers) and renderdoc_get_texture_thumbnail (for thumbnails only).
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 retrieving texture lists, but doesn't explicitly state when to use this vs alternatives like renderdoc_search_resources (which might filter differently) or renderdoc_get_capture_info (which provides general info). No explicit when-not or alternative guidance is provided.
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 full burden. It mentions the output is a 'base64 encoded thumbnail image' and implies it's for 'quickly visualizing,' but lacks details on performance (e.g., speed, size limits), error handling, or whether it requires specific states (like an active capture). This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and followed by a brief utility note. Every sentence earns its place without redundancy, making it efficient and easy to parse.
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 3 parameters with full schema coverage and no output schema, the description adequately covers the tool's purpose and output format. However, as a tool with no annotations, it lacks behavioral details (e.g., performance, errors) and does not explain the return value beyond 'base64 encoded thumbnail image,' leaving gaps in completeness for practical 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?
Schema description coverage is 100%, so the schema already documents all parameters (texture_id, mip, max_size) with descriptions. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining texture_id format or mip level implications. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get a base64 encoded thumbnail image') and resource ('of a texture'), distinguishing it from siblings like renderdoc_get_textures (which likely lists textures) or renderdoc_get_pixel_data (which might retrieve raw pixel data). It adds value by specifying the output format (base64 encoded) and purpose (visualization).
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 context ('Useful for quickly visualizing texture contents'), suggesting this tool is for previews rather than detailed analysis. However, it does not explicitly state when to use this vs. alternatives like renderdoc_get_textures or renderdoc_get_pixel_data, nor does it mention prerequisites (e.g., needing an open capture).
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 the full burden of behavioral disclosure. It mentions the return of 'basic information' but does not detail critical aspects such as error handling, file format requirements, performance implications, or whether the capture remains open for subsequent operations. This leaves significant gaps in understanding 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?
The description is two sentences, front-loaded with the core action and followed by return details. Every sentence adds value: the first defines the purpose, and the second specifies the output. There is no wasted text, making it efficient and well-structured.
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's moderate complexity (opening a file for analysis) and no output schema, the description covers the basic purpose and return information but lacks details on behavioral traits, error cases, or integration with sibling tools. It is minimally adequate but has clear gaps in completeness for effective agent 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 has 100% description coverage, with 'file_path' clearly documented as 'Absolute path to the RDC file to open.' The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without extra 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 clearly states the action ('Open an RDC capture file for analysis') and the resource ('RDC capture file'), distinguishing it from siblings like 'renderdoc_close_capture' (closing) or 'renderdoc_get_capture_info' (retrieving info). It specifies the tool's function precisely without being vague or tautological.
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 context by stating it 'Returns basic information about the capture,' suggesting it's for initial analysis, but does not explicitly state when to use it versus alternatives like 'renderdoc_get_capture_info' or prerequisites. This provides some guidance but lacks explicit comparisons or exclusions.
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 the full burden. It discloses that the tool returns 'availability status and installation info', which gives some behavioral insight into what to expect. However, it lacks details on potential errors (e.g., if RenderDoc is not installed), performance implications, or system requirements, leaving gaps in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose and expected output. It is front-loaded with the main action ('Check if RenderDoc is available') and avoids unnecessary details, making it easy to understand quickly without 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?
Given the tool's low complexity (0 parameters) and no output schema, the description is moderately complete. It explains what the tool does and what it returns, but without annotations or an output schema, it lacks details on the format of the return values (e.g., what 'availability status' includes) and error handling, which could be helpful for an AI agent.
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 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description does not mention any parameters, which is appropriate here. It adds value by explaining the tool's purpose and return information, compensating for the lack of parameter documentation in 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?
The description clearly states the specific action ('Check if RenderDoc is available') and the resource ('on the system'), with explicit mention of what it returns ('availability status and installation info'). It distinguishes itself from sibling tools like 'renderdoc_analyze_draw_call' or 'renderdoc_open_capture' by focusing on system availability rather than capture analysis or manipulation.
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 usage context by stating it checks 'availability on the system', suggesting it should be used to verify RenderDoc installation before attempting other operations. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as using this tool first before invoking others that require RenderDoc to be available.
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 full burden. It discloses that the tool releases resources, which is a key behavioral trait beyond just closing. However, it lacks details on side effects, error conditions, or confirmation of success, leaving gaps in transparency for a tool that likely involves resource management.
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 a single, efficient sentence that front-loads the core action ('Close the currently open RDC capture file') and adds necessary context ('and release resources'). There is zero waste, making it highly concise and well-structured for quick understanding.
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's simplicity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose and resource release, yet lacks information on what happens after closing (e.g., state changes, error handling), which could be useful for an agent in a workflow with sibling tools like 'renderdoc_open_capture'.
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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing on the tool's action. A baseline of 4 is applied as it effectively handles the lack of parameters without unnecessary detail.
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 specific action ('Close') and resource ('currently open RDC capture file'), distinguishing it from sibling tools like 'renderdoc_open_capture' and 'renderdoc_analyze_draw_call'. It precisely defines what the tool does without being vague or tautological.
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 usage context by specifying 'currently open RDC capture file', suggesting it should be used when a capture is open. However, it does not explicitly state when not to use it or name alternatives, such as whether it's required before opening another capture or if other tools can be used while a capture is open.
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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