RenderDoc MCP Server

by halby24

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool serves a distinct purpose with clearly defined search criteria or actions. For example, find_draws_by_resource, find_draws_by_shader, and find_draws_by_texture are all differentiated by the search parameter, and get_draw_calls vs get_draw_call_details separate listing from details.
Naming Consistency4/5
Most tools follow the verb_noun pattern (e.g., get_draw_calls, list_captures, open_capture), but there is minor inconsistency: 'find_draws_by_resource' vs 'get_draw_calls' (different verbs), and 'get_action_timings' vs 'get_draw_call_details' (different nouns for similar concepts).
Tool Count4/5
15 tools is appropriate for a graphics debugger MCP server, covering capture management, draw call queries, resource inspection, pipeline state, and timing. Slightly more than minimal but well-scoped.
Completeness4/5
The tool set covers the main inspection workflow: open capture, list actions, get details, check pipeline state, shaders, buffers, textures, and timings. Missing features like capturing a frame or stepping through events are expected external to MCP.
Average 4.1/5 across 15 of 15 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
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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 implies a read-only operation through the 'Get' verb and lists the return values, but it does not explicitly state behavioral traits such as absence of side effects, permission requirements, or limitations. Since no annotations are provided, the description partially covers transparency but lacks explicit statements.
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 concise and front-loaded with the core purpose, followed by a bulleted list of return fields. It avoids unnecessary words, though the bullet list could be slightly more compact.
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 details the returned statistics but does not mention prerequisites (e.g., that a capture must be currently loaded) or state conditions. Given the tool's simplicity (no parameters), the description is mostly complete but could benefit from contextual guidance.
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 tool has no parameters, and the input schema coverage is 100% (vacuously). The description correctly omits parameter details, so the baseline score of 3 applies.
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 'Get a summary of the current capture frame' and lists the statistics returned, clearly indicating the tool's purpose and what it provides. It distinguishes itself from sibling tools that focus on specific aspects (e.g., get_draw_call_details) by offering a high-level overview.
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 does not provide any guidance on when to use this tool versus alternatives, nor does it mention prerequisites or conditions for use. There is no explicit 'when to use' or 'when not to use' advice.
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 cover behavioral traits. It mentions that the tool returns metadata (dimensions, format, mip levels) but does not disclose side effects, required permissions, or performance characteristics. The read-only nature is implied but not explicitly stated.
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 concise with two sentences and an Args section. It is front-loaded with the main purpose. However, the Args section redundantly restates the schema parameter; a bullet list under a heading adds structure but could be integrated better.
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 presence of an output schema (which defines return structure) and one parameter, the description is minimally complete. It fails to mention error handling (e.g., invalid resource_id) or that the tool requires a previously captured frame. Could be improved with a note on prerequisites.
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 single parameter resource_id has 0% schema description coverage, but the description adds 'The resource ID of the texture', providing basic semantics. However, it does not clarify expected format or constraints (e.g., must be a valid texture ID). This is minimally adequate for a single parameter.
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 'Get metadata about a texture resource' and elaborates with specific properties (dimensions, format, mip levels). This distinguishes it from sibling tools like get_texture_data, which likely returns pixel data, and get_shader_info for shaders.
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 (e.g., get_texture_data) or any prerequisites (e.g., ensure the texture exists). It solely describes what it does without contextual usage advice.
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?
The description explicitly states this is a read operation ('Read the contents'), implying non-destructive behavior. However, with no annotations, it does not disclose potential side effects, error handling, or constraints like buffer size limits or required resource state.
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 with three sentences and an Args section. It is well-structured, front-loaded with the purpose, and every sentence adds value without redundancy.
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 3 parameters and presence of an output schema (assumed to explain return details), the description adequately covers the tool's behavior. It explains parameters and notes the return format. Could be improved by mentioning error cases or performance implications, but overall complete for a read operation.
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 description coverage is 0%, so the description compensates fully. It defines all three parameters: resource_id as 'The resource ID of the buffer to read', offset as 'Byte offset to start reading from', and length as 'Number of bytes to read', including default values. This adds crucial meaning beyond the schema's type-only definitions.
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 'Read the contents of a buffer resource' with a specific verb and resource type. It distinguishes from sibling tools like get_texture_data by focusing on buffers, and explicitly mentions the return format (base64-encoded bytes with metadata).
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. Sibling tools like get_texture_data or get_draw_calls exist, but the description does not differentiate use cases or mention any preconditions.
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 provided, so description must stand alone. It explains parameters and return type (hierarchical tree). However, it does not state that it is read-only, nor any performance implications or authorization needs. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Starts with clear purpose, then parameter list in a structured docstring format, followed by return description. Slightly lengthy but each sentence adds value. Could be more concise without losing information.
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?
Given 7 parameters, no annotations, and presence of output schema, the description fully covers what the tool does, its filtering capabilities, and hierarchical return. No obvious gaps.
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%, yet description lists each parameter with its meaning, type, default, and behavior (e.g., partial match for marker_filter). Adds substantial value beyond the input 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?
Describes exactly what the tool does: get list of draw calls and actions hierarchically. Clearly distinguishes from siblings like get_draw_call_details and find_draws_by_* which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this vs alternatives. Does not mention that for details on a specific call one should use get_draw_call_details, or for resource-based filtering use sibling tools. Relies entirely on tool name and parameter names for context.
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 exist, so the description carries the full burden. It correctly states that it returns a list of capture files with metadata (filename, path, size, time). However, it does not disclose whether subdirectories are searched, what happens if the directory is invalid or empty, or if there are performance considerations. The return format is partially described.
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 fairly concise with a clear summary, an Args section, and a Returns section. It is well-structured and easy to read. A slight improvement could be merging the Args and Returns into a more compact form.
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 (one parameter, no annotations, output schema exists), the description is mostly complete. It lists the return metadata fields. However, it could mention default behavior (e.g., non-recursive, sorted by name) and error handling.
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 only parameter 'directory' is defined in the schema as a string with no description. The tool description adds: 'The directory path to search for capture files.' This clearly explains the parameter's role and expected input, adding significant meaning beyond 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 'List all RenderDoc capture files (.rdc) in the specified directory.' It uses a specific verb 'List', identifies the resource 'capture files', and specifies the scope (in a directory). This distinguishes it from sibling tools, which focus on other aspects like draws, buffers, or pipeline state.
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 does not provide explicit guidance on when to use this tool versus alternatives. It is implied by the tool's name and purpose that it is for listing captures, but there is no mention of when not to use it or what prerequisites exist (e.g., directory must exist).
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 bears full responsibility. It discloses that GPU timing counters may not be available on all hardware/drivers, sets expectations for return structure, and implies read-only behavior. However, it does not mention potential side effects or detailed error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with Args and Returns sections, making it easy to scan. It is front-loaded with the main purpose. Some phrases like 'typically seconds' could be streamlined, but overall it is concise and informative.
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?
Given the tool has an output schema (not shown), the description still thoroughly describes return fields including available, unit, timings with fields, total_duration_ms, and count. It also covers all parameters and edge cases (optional parameters, partial match, exclusion). No gaps for a simple retrieval tool.
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 coverage is 0%, so the description must add meaning. It explains each parameter: event_ids (optional, default null means all), marker_filter (partial match string inclusion), exclude_markers (list of strings to exclude). This adds context beyond the raw schema, such as the default behavior and the nature of the filters.
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 starts with a clear verb ('Get') and resource ('GPU timing information for actions (draw calls, dispatches, etc.)'). It succinctly states the tool's purpose and distinguishes it from sibling tools like find_draws_by_resource or get_draw_call_details, which focus on different aspects of draw calls.
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?
While the description explains how to use the tool via parameters (event_ids, marker_filter, exclude_markers), it does not explicitly state when to choose this tool over alternatives, nor does it mention when not to use it. The context of sibling tools implies it's for timing, but no direct guidance is given.
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 adds that response includes 'vertex/index counts, resource outputs, and other metadata', but does not disclose if it's read-only, permissions needed, or side effects, which are minimal for a get 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?
Two sentences plus an elegantly placed Arg section. Front-loaded purpose, no redundant wording, efficient structure.
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?
Single parameter, no annotations, but has output schema (unseen). Description mentions included data types, which is sufficient for a simple retrieval tool. Could hint at output format, but not essential.
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 has `event_id` with 0% description coverage. The description adds 'The event ID of the draw call to inspect', clarifying the parameter's role and compensating for the schema's lack of 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 states 'Get detailed information about a specific draw call', which is a specific verb+resource combination. It distinguishes from sibling `get_draw_calls` (plural) that likely lists calls, and other find-style 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 implies usage when one has an `event_id` for a specific draw call, and context from siblings suggests it's for drill-down after listing. No explicit when-not or alternatives, but clear enough.
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 carries the full burden. It explicitly lists the returned components (shaders, resources, samplers, etc.), making the behavior clear. It does not disclose any side effects, but as a read operation, the 'Get' verb implies non-destructive access.
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 a clear header and a bulleted list of return details. It is reasonably concise given the complexity of the return value, though the list could be slightly more compact.
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?
Given the complexity of a graphics pipeline state and the absence of annotations, the description provides a thorough breakdown of what is returned (shaders, resources, UAVs, samplers, etc.). The existence of an output schema is noted, but the description itself is sufficiently complete for an AI agent to understand the tool's output.
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?
Although the input schema has 0% description coverage, the description adds meaning by stating 'The event ID to get pipeline state at,' which clarifies the purpose of the only parameter (event_id). This compensates for the schema's lack of description.
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 'Get the full graphics pipeline state at a specific event,' specifying the verb (Get) and resource (pipeline state). It distinguishes itself from sibling tools like get_draw_call_details or get_shader_info by focusing on the complete pipeline state.
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 does not provide explicit guidance on when to use this tool versus alternatives. It implies usage for retrieving pipeline state at an event, but lacks instructions on when not to use it or what distinguishes it from similar tools like get_draw_call_details.
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 full burden. It details behavioral aspects: can read specific mip levels, slices (with cube map index mapping), MSAA samples, and depth slices for 3D textures. It also mentions the return format (base64-encoded bytes with metadata). However, it lacks details on performance or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph with clear 'Args' section. It is efficient and front-loaded, though slightly longer than ideal. Every sentence serves a purpose.
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 5 parameters, no annotations, and an output schema mentioned, the description covers parameter semantics and return value adequately. However, it lacks usage guidelines and does not mention potential limitations (e.g., data size). It is mostly complete for a read operation.
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 description coverage is 0%, so the description must add meaning. It explains each parameter: 'resource_id', 'mip', 'slice' (with cube map index mapping), 'sample', and 'depth_slice' (for 3D textures, with behavior when specified). It also states defaults, adding significant value beyond 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 'Read the pixel data of a texture resource', providing a specific verb and resource. It distinguishes from siblings like 'get_texture_info' (which likely returns metadata) by focusing on pixel data retrieval.
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 reading pixel data but does not explicitly state when to use this tool over alternatives like 'get_texture_info' or 'find_draws_by_texture'. No when-not-to-use guidance is provided.
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 carries full burden. It accurately describes a read-only check returning status and API type, with no mentioned side effects. It is transparent enough for such a 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 two sentences, concisely stating purpose and return value with no unnecessary words. Front-loaded and efficient.
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 parameterless tool with an output schema, the description explains the return includes capture status and API type. This is complete given context signals and the existence of an output schema.
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?
There are zero parameters, so schema coverage is trivially 100%. The baseline score of 4 applies; the description does not need to add parameter information.
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 checks if a capture is loaded in RenderDoc and returns status and API type. It distinguishes from sibling tools like get_draw_calls or get_pipeline_state which have more specific purposes.
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 use for checking capture status but does not explicitly state when to use it vs alternatives like list_captures or open_capture. No when-not or exclusion guidance is provided.
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 carries the full burden. It discloses that the tool returns shader disassembly, constant buffer values, and resource bindings, providing key behavioral info. However, it does not mention safety, permissions, or side effects, but for a read-like info tool this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with a one-sentence main purpose followed by a structured Args list. Every sentence is necessary and front-loaded, with no redundancy or wasted 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?
Given the presence of an output schema, the description need not detail return structure, but it lists the return content (disassembly, constant buffer, bindings). It covers inputs and outputs adequately but lacks edge cases, event validity, or why to choose this tool over similar ones. Still, it is reasonably complete for the tool's simplicity.
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 input schema has 0% description coverage, so the description must fully explain each parameter. It does so effectively: event_id is described as 'The event ID to inspect the shader at' and stage as 'The shader stage' with an enum listing. This adds meaning beyond the schema's type and required status.
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 gets shader information for a specific stage at a given event, specifying the verb 'Get' and the resource 'shader information'. It distinguishes from siblings like get_pipeline_state by focusing on per-stage shader details at a specific event, making its purpose unique.
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 but does not explicitly state when to use this tool over alternatives like get_pipeline_state or when not to use it. No guidance on prerequisites or exclusions is provided, though the purpose is clear enough for basic selection.
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?
The description discloses that it searches across SRVs, UAVs, and render targets, which adds behavioral context beyond the tool's name. It also mentions the return format (list with event IDs and match reasons). No annotations were provided, so the description handles this well.
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 three short sentences. The main purpose is front-loaded, followed by parameter details and return value description. Every sentence adds value.
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 that an output schema exists (though not shown), the description appropriately summarizes the return format. It covers the search scope and return details sufficiently for a tool with one parameter.
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?
With 0% schema description coverage, the description compensates by explaining the parameter 'texture_name' as 'Partial name to search for in texture resource names', adding meaningful interpretation beyond the schema's type definition.
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 'Find', the resource 'draw calls', and the scope 'using a texture with the given name (partial match)'. It effectively distinguishes itself from sibling tools like find_draws_by_resource and find_draws_by_shader.
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 specifies that the search is a partial match on the texture name, which guides usage. However, it does not explicitly mention when to use this tool versus siblings, but the purpose is clear enough.
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?
Given no annotations, the description carries full burden. It discloses the crucial side effect that opening a new capture closes the current one. It also mentions the file format .rdc. Could be improved with more detail on return values.
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 purpose, and contains no redundant information. Every part earns its 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 there is an output schema, the description need not detail return values. It mentions success status and info, which is sufficient. The side effect is covered. Could briefly note file existence or permission requirements, but 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?
Schema coverage is 0%, so the description must add meaning. It describes 'capture_path' as 'Full path to the capture file to open', which clarifies the parameter beyond just its type string.
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 'Open a RenderDoc capture file (.rdc)', which is a specific verb+resource. It distinguishes from siblings like list_captures or find_draws_by_resource, which are about listing or analyzing captures, not opening them.
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 when to use (to open a capture) and notes the side effect of closing any currently open capture, but does not explicitly state when not to use or mention alternatives. However, the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It fully discloses the search scope (shaders, SRVs, UAVs, render targets, depth targets) and the return structure (list of matching draw calls with event IDs and match reasons). No hidden behaviors or contradictions.
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 a clear opening line followed by an Args section. It is not overly long, but the Args section repeats the parameter name unnecessarily. Slightly redundant but still effective.
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 single-parameter search tool with an output schema, the description covers purpose, parameter format, search scope, and return information. No additional details are needed for 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?
Schema coverage is 0%, so the description must compensate. It provides an example format (e.g., 'ResourceId::12345' or '12345'), adding meaning beyond the blank schema. However, it could include more detail on allowed patterns or constraints.
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?
Starts with a clear verb+resource: 'Find all draw calls using a specific resource ID (exact match)'. Immediately distinguishes from sibling tools like find_draws_by_shader and find_draws_by_texture by specifying the search key.
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 context by stating exact match and listing search targets (shaders, SRVs, etc.), but does not explicitly state when to use this tool versus alternatives or when not to use it. The differentiation from siblings is implied by the parameter.
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 full burden. It explains the matching behavior (partial match), the search domain (shader names or entry points), and the return structure (list with event IDs and match reasons). It does not mention performance or limitations, but the query nature is clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a short paragraph followed by a bullet-like list for args. It is front-loaded with the main purpose in the first sentence, and every line provides necessary information without redundancy.
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?
Given an output schema exists (context signals), the description need not detail return fields, but it still summarizes 'list of matching draw calls with event IDs and match reasons.' Parameters are fully explained, and the tool's purpose is clear in the context of sibling tools.
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 description coverage is 0%, so the description must explain the parameters. It does so clearly: shader_name is 'Partial name to search for in shader names or entry points', and stage is 'Optional shader stage to search (if not specified, searches all stages)'. This adds meaning beyond the schema 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 begins with 'Find all draw calls using a shader with the given name (partial match).' This specifies the verb 'find' and the resource 'draw calls using a shader', and the partial match detail differentiates it from sibling tools like find_draws_by_resource or find_draws_by_texture.
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 purpose is clear from the name and description, implying when to use this tool over siblings. However, it lacks explicit when-not or alternative suggestions, such as stating to use other find_draws tools for resource-based searches.
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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