openmedia-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool targets a distinct media type and action, with clear boundaries (e.g., pdf_read vs pdf_ocr vs pdf_view explicitly address text layer vs scanned). The media_probe helper reduces ambiguity by routing to the correct tools. No two tools appear to do the same thing.
Naming Consistency5/5All tools follow a strict pattern: media type prefix (image_, pdf_, video_, audio_) followed by a descriptive verb (view, info, transform, read, convert, clip). All snake_case, no mixed conventions, and the pattern makes tool purposes predictable.
Tool Count4/522 tools is on the heavy side, but the server covers four distinct media domains (image, PDF, video, audio) each with a logical set of operations. Every tool earns its place given the broad scope, though it slightly exceeds the typical comfortable range.
Completeness5/5The surface covers the full lifecycle for each media type: inspection (info/probe), viewing/reading, transformation/conversion, extraction (OCR, transcript, frames, audio), and editing (clip, trim). No obvious dead ends or critical missing operations for the stated purpose.
Average 4.2/5 across 22 of 22 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- 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 does disclose meaningful behavior: 'stream copy, lossless' (implying no re-encoding, with possible format limits), that a new file is created (original untouched), and the default output path. However, it doesn't address error behavior, whether start must precede end, or supported source formats.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Purpose is front-loaded in the first line, followed by a compact, well-organized arg list. Efficient and scannable, with no filler, though the args section could arguably be trimmed into prose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Parameter coverage is complete and an output schema exists to document return values. But given no annotations and it being a file-producing mutation tool, it omits practical constraints like stream-copy format compatibility, missing-file errors, and range validation. Adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description fully compensates by explaining all four parameters: source (local path or URL), start (seconds or MM:SS/HH:MM:SS), end, and output_path (with default). This adds real meaning beyond the bare string schema, which otherwise offers no format hints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb+resource ('Cut a section out of an audio file into a new file') and adds the lossless stream-copy detail. The purpose is unmistakable, though it only implicitly differentiates itself from audio_convert (conversion) and video_clip (video) rather than naming them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus its siblings. It never contrasts with audio_convert (which also deals with audio output files), video_clip (the video analog), or audio_waveform. No exclusions or alternative conditions are given; usage is only implied by the name.
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 the 'without downloading' behavior for URLs, which is valuable, but omits other relevant traits like potential network dependencies, permissions, failure modes, or whether operations are read-only in an explicit manner. It provides some context but not comprehensive behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a clear opening sentence, a short breakdown of metadata by source type, and a compact Args section. Every sentence contributes to understanding without redundancy or verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists (though not shown), the return value structure is presumably covered elsewhere. The description effectively explains the two input scenarios and what metadata to expect, covering the key usage context. Minor gaps like error handling or limitations are not critical for a metadata tool and can be assumed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It defines the sole parameter 'source' as either a local file path or a video page URL, giving concrete examples (YouTube, Vimeo). This adds meaningful semantics beyond the bare schema, though it does not delve into format constraints or edge cases.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: getting video metadata, and it delineates two source types (local files vs URLs) with distinct metadata fields. While it does not explicitly differentiate from siblings like media_probe, the verb+resource is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says what the tool does for local files and URLs, but provides no guidance on when to choose this over other sibling tools such as media_probe or video_download. It does not state exclusions, prerequisites, or alternative conditions, so an agent has to infer when this is the right choice.
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 responsibility. It notes that a composite is created, but fails to disclose what happens to the composite (returned, saved, displayed) or any side effects. It also lacks details on error behavior or required permissions for reading image sources. This is a significant gap for a tool without structured safety hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact: two sentences stating purpose and then a clear parameter list. Every sentence earns its place, and the purpose is front-loaded. No redundant phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should clarify what the tool returns or produces. It only says 'View' without explaining whether the composite is returned as an image, saved, or displayed inline. It also omits any mention of error handling or constraints on input sizes. For a tool that generates a composite, this is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must define each parameter. It does so effectively: source_a and source_b as image paths or URLs, and max_dimension as the longest edge with a default. This adds meaning beyond the schema's bare titles and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (View) and resource (two images side by side in a single composite), with an explicit purpose of visual comparison. This distinguishes it from sibling tools like image_view or image_transform without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for visual comparison' implies a use case, but the description does not explicitly state when to use this tool versus alternatives, nor does it provide any exclusions or conditions. It offers only implicit 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the tool uses a local Whisper model and requires an optional dependency, which is useful. However, it does not disclose output structure, error behaviors, or resource implications like model download on first run. It remains a basic but acceptable disclosure for a non-destructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized: purpose first, then dependency installation, then a parameter list. It is reasonably concise and front-loads the core function. The only minor inefficiency is the dependency sentence could be shortened, but it is essential for usability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (as indicated by context signals), the description need not explain return values. It covers the required dependency, parameter semantics, and local processing. The absence of error or limitation mentions is minor for a relatively simple transcription tool. It seems complete enough for an agent to call correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context to each parameter beyond the schema. 'source' is explained as a local file path or direct URL, 'language' includes examples and auto-detect behavior, and 'max_chars' clarifies the default cap meaning. This compensates for the 0% schema coverage and provides actionable usage details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Transcribe speech in an audio file to text using a local Whisper model (faster-whisper).' It specifies a distinct verb (transcribe), a resource (audio file), and the method (Whisper). This is specific enough to distinguish from sibling tools like audio_convert or audio_trim, and even video_transcript, which targets video files.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides installation prerequisites but no guidance on when to use this tool versus alternatives. It does not mention video_transcript for video files, nor does it specify what formats are supported or any conditions that would make this tool inappropriate. The only context is the tool name and purpose, which is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It does not state whether the tool performs network I/O for URLs, whether it is read-only, or any side effects or limitations. The description only states its purpose without revealing operational traits, which is insufficient given zero 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded with the core purpose, followed by a clear usage directive and a simple args listing. Every sentence serves a purpose with zero waste, making it an efficient and well-structured definition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple dispatcher tool with one parameter and an output schema presumably describing the media type and qualifying tools, the description is adequately complete. It covers the core purpose, usage trigger, and parameter meaning. Minor details like error handling or network implications are absent but not critical given the output schema's existence and the tool's straightforward nature.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description explicitly defines 'source' as a 'Local file path or URL', adding meaning beyond the bare schema string type. However, it does not elaborate on accepted formats, size limits, or network behavior, providing only minimal semantic enhancement.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool identifies media type and routes to appropriate tools, with a specific verb ('identify') and resource ('file or URL'). It implies differentiation from siblings by positioning itself as the first step when media type is unknown, without needing to list every sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
'Use this first when the media type is unknown' explicitly defines when to use the tool. It does not explicitly state when not to use it or name alternatives, but the implication that known media types should skip this tool is clear. This is slightly less than a full 5 because it lacks explicit exclusions or alternative naming.
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 of behavioral disclosure. It reveals meaningful traits: supports both local files and URLs, defaults output to ~/Downloads/openmedia/, defaults format to mp3, and supports section extraction via start/end. Left undisclosed are error behaviors (e.g., handling of unsupported input formats), overwrite policy for existing files, and time format requirements. The disclosure is decent 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
A clean docstring format with a one-line purpose statement followed by a compact 5-parameter Args block. Each line earns its place, and the most important information (what the tool does) is front-loaded. Minor inefficiency exists in repeating parameter names already in the schema, but this is standard docstring practice and doesn't bloat the content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, return-value explanation isn't needed. However, the description omits the start/end time format (critical for correct parameter use), doesn't note supported input video formats, and doesn't differentiate when to choose extraction over audio_convert or audio_trim. For a 5-parameter tool with one required field, these gaps are notable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates. It enriches every parameter: source is clarified as 'Local file path or video URL,' output_format enumerates all 5 valid values plus the default, output_path explains the default location, and start/end describe their purpose. A real gap is the missing time format for start/end (seconds vs. HH:MM:SS), which hinders correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Extract the audio track from a video into an audio file.' This clearly distinguishes it from siblings like audio_convert (conversion of existing audio), audio_trim (trimming audio), and video_clip (video editing). An agent can immediately identify the extraction function without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The parameter descriptions provide implied context — 'Optional start time to extract only a section' suggests partial extraction, and 'Local file path or video URL' tells users what inputs are valid. However, there is no explicit guidance on when to use this tool versus siblings like audio_convert or audio_trim, nor any exclusion criteria or alternative routing.
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 must carry the burden. It states it 'gets' metadata, which implies a read-only operation, but does not explicitly confirm no side effects, error behavior, or permissions. The description is sufficient for a simple info tool, but more detail (e.g., 'reads only, does not modify file') would be more transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus a terse Args block. Every word earns its place: the purpose is front-loaded, the metadata list is specific, and the parameter description is compact. No redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists (not shown), the return format is presumably structured, and the description lists the expected metadata fields. It covers the source parameter adequately. Missing details like error handling or network requirements for URLs are minor for a metadata tool; overall it is complete enough for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The Args section explains that 'source' can be a local file path or an HTTP(S) URL, which adds meaning beyond the plain schema type. The schema coverage is 0%, so this explanatory text is crucial; it compensates well and gives the agent the necessary context for the only parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Get') and resource ('audio file metadata') and enumerates the exact metadata fields (codec, duration, sample rate, channels, bitrate, embedded tags). This clearly distinguishes it from siblings like audio_convert or audio_trim, which perform transformations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it (when you need metadata about an audio file) but does not explicitly contrast it with alternatives or state when not to use it. With many sibling audio tools, explicit routing would improve clarity, but the intent is inferable from the verb and field list.
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 what the tool returns and that it can accept a URL, implying network access. However, it does not mention potential latency, errors on inaccessible sources, or any side effects, which are relevant for an agent deciding whether to call it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with an Args section, front-loaded with the purpose. Every sentence adds value; there is no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema exists, so the description need not detail return values. It covers the purpose, input, and a summary of what metadata is extracted. Missing error handling or performance notes, but for a single-param read-only tool, it is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema only defines 'source' as a string with 0% description coverage. The description compensates by explaining it accepts a local file path or direct http(s) URL, adding meaningful semantics beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does 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 'image metadata', enumerating specific items (format, dimensions, mode, file size, EXIF summary). It also distinguishes from viewing by saying 'without viewing it', which differentiates it from sibling image_view and media_probe.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'without viewing it' implies it is an alternative to viewing tools, but it does not explicitly name any sibling or give conditions for when to use this tool over image_ocr, image_transform, or media_probe. Usage context is implied but not directly stated.
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 mentions Tesseract OCR, which gives a hint about behavior, but does not disclose limitations (e.g., supported image formats, accuracy on different image types, or rate limits). The description covers input parameters but lacks deeper behavioral context that would help an agent judge reliability 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a compact docstring that front-loads the core purpose and then lists parameters in a clear, structured format. Every sentence provides value; there is no fluff or repetition. It is appropriately concise and well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists, the description need not explain return values. It covers all parameters and includes practical details like URL support and language code formatting. However, it does not mention any constraints on image inputs (e.g., size limits, file type support) or performance characteristics, which would enhance completeness for a tool with no annotation context. Overall, it is nearly complete for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, but the description provides a detailed 'Args' section explaining each parameter (source, language, max_chars) including examples for language codes and the meaning of max_chars. This adds significant meaning beyond the bare schema fields, fully compensating for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Extract' and the resource 'text from an image', and specifies the OCR engine (Tesseract). This distinguishes it from siblings like pdf_ocr (which handles PDFs) and other image tools that do not perform OCR. The purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for images but does not explicitly state when to use this tool over alternatives such as pdf_ocr or other image processing tools. There is no reference to alternatives or exclusion criteria, leaving the agent to infer context from the name and siblings. This is adequate but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavior. It states the operation (download via yt-dlp), the output (file path), and the default output location. However, it does not disclose potential side effects such as network latency, disk usage, or error conditions (e.g., invalid URL). It also does not mention if the file is overwritten. This is adequate but not thorough, so a 3 is appropriate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: a clear first sentence stating the core function, followed by supporting details and a bulleted list of arguments. It is not excessively verbose, though the Args block could be more compact. The main purpose is front-loaded, making it easy for an agent to quickly grasp the tool's role. Slightly high length, but efficient overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a download tool with four parameters and an output schema (which presumably indicates the return value), the description covers the purpose, parameter semantics, defaults, and return value. It does not mention error handling or prerequisites (like network access), but these are inherently implied. The description is complete enough for an agent to call the tool correctly without additional information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. It does so by explaining each parameter: source (URL), resolution with specific options ('480p', '720p', '1080p', 'best'), audio_only (meaning and format m4a/opus), and output_path (target path with default). This adds significant semantic meaning beyond the schema's types and defaults. It could mention that resolution is ignored if audio_only is true, but it's not critical; a 4 is justified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Download a video (or just its audio) from a URL to disk via yt-dlp.' It identifies the target resource (video/audio from URL) and the mechanism (yt-dlp), and mentions the return value. This distinguishes it from siblings like video_extract_audio (which works on local files) and video_info (which inspects) — the verb 'download' is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context that this tool is for downloading from URLs ('Supports YouTube and 1000+ other sites'), which implies web sources. It does not explicitly name alternatives or state when not to use it, but the context is clear enough. The absence of exclusion is a minor gap; a 4 is warranted because the purpose is clear and the agent can infer when to use it.
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 the output (an image), the source type (local path or URL), and the split_channels behavior. However, it does not mention potential limitations (e.g., unsupported formats, network requirements) or side effects beyond rendering, and it does not explicitly state that the operation is non-destructive. Adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences of purpose followed by a clean Args list. No redundant or filler content. The most important information (what it does, when to use) is front-loaded, and the parameter definitions are compact and scannable. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with 4 parameters and no output schema, the description covers the essential aspects: purpose, usage context, and parameter semantics. It does not specify the image format or error behaviors, but these are minor given the tool's simplicity. An agent can call it correctly without further information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the Args section in the description is the only documentation for parameters. It explains each of the 4 parameters with type, default, and meaning (e.g., 'width: Image width in px (default 1024)'). This fully compensates for the empty schema and adds significant value beyond the bare schema properties.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Render') and resource ('audio waveform') with a clear output ('image the model can see'). It also lists concrete use cases (spotting silence, clipping, loudness structure, speech vs music), making it distinct from siblings like audio_info or audio_convert. No tautology; purpose is immediately apparent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Useful for...' explicitly describes when to use the tool, giving context for silence detection, clipping analysis, etc. However, it does not mention alternatives or when not to use it, leaving some inference to the agent. Still, the use-case list is strong enough to guide selection.
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?
Annotations are absent, so the description carries the full burden. It discloses the page cap and default behaviors for dpi, max_dimension, and quality. It also states that output is images the model can see. However, it does not mention error behavior, what happens with invalid sources, or the exact return format (e.g., URLs vs. embedded images). This is adequate but leaves some behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is tightly structured: a one-sentence purpose, a caveat on page caps, and a clean argument list. Every sentence earns its place. The front-loaded purpose and explicit parameter guidance make it efficient to scan and act on.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description should clarify what the tool returns. It states images are rendered and viewable, but does not specify how those images are delivered (inline, URLs, base64) or how errors are surfaced. For a rendering tool, that's a small gap. However, the parameter documentation is thorough and covers the main operational details, so it's nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must fully explain all parameters. It does: source (file path or URL), pages (with examples), dpi (range and default), max_dimension (meaning and default), and quality (range and default). Each parameter is given operational meaning beyond the bare type declaration, making it easy for an agent to set values correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a specific verb-resource pair: 'Render PDF pages as images the model can see.' It immediately clarifies the tool's purpose as visual rendering and lists concrete use cases (scanned documents, figures, charts, complex layouts) that distinguish it from sibling tools like pdf_read, pdf_ocr, and pdf_info. The purpose is unambiguous and well-differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear when-to-use context ('Use for scanned documents, figures, charts, complex layouts, or to verify visual appearance.') and includes a practical batching instruction ('Capped at 8 pages per call to protect context; request ranges in batches.'). It stops short of explicitly naming alternatives or stating when not to use, but the usage guidance is strong enough to assign a clear selection path.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It mentions performance (slower than pdf_read) and a hard limit (capped at 10 pages per call), which are useful. It does not disclose output format, but since an output schema exists, that is covered. It also implies OCR quality and language support via parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured: a single-sentence overview, a comparative note, a explicit cap, and a bulleted Args list. No redundant or filler sentences; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters and no annotations, the description covers all parameters and key operational limits (page cap, speed). It doesn't mention edge cases like error handling or unsupported formats, but the presence of an output schema mitigates the need to describe return structure. Minor gap: no mention of authentication or file access prerequisites.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully explain parameters. It does: each of the five parameters (source, pages, language, dpi, max_chars) is described with type, default, and purpose in the Args section, adding significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('OCR'), resource ('scanned PDF pages'), and method ('using Tesseract'), and explicitly contrasts with the sibling tool pdf_read (works on PDFs without a text layer, slower). This clearly distinguishes it from similar tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear usage context: it works on PDFs without a text layer and is slower than pdf_read, implicitly guiding when to choose this over pdf_read. However, it doesn't explicitly state 'use this when...' or mention alternatives like image_ocr, so it's not fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full behavioral burden. It discloses that remote videos are fetched once at <=720p and cached, that frames are model-visible images, and that there is a 12-frame cap per call. It does not explicitly state read-only status or potential failure modes, but the disclosed behaviors are substantial and honest, covering key operational constraints such as caching and resolution limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized: a one-line purpose, a contextual note, usage modes, a constraint, then a clean Args block. Every sentence adds value—no fluff or repetition. The front-loaded purpose and usage guidance make it easy to skim, and the parameter documentation is compact yet complete.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, various time formats) and lack of an output schema, the description covers the essential details: how to specify frames, window constraints, and output form ('model-visible images'). It leaves out exact return structure (e.g., whether images are markdown or base64) and error handling, which could matter for an agent, but overall it provides enough to use the tool confidently for typical video-frame extraction tasks.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% parameter descriptions, so the description completely compensates by providing a detailed Args section. Each parameter gets a purpose, default where applicable, and format examples (e.g., '0:05, 1:30, 95.5' for timestamps, 'seconds or MM:SS' for start/end). This fully bridges the gap left by the schema and makes the tool callable correctly with minimal agent effort.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear purpose: 'Extract frames from a video and return them as model-visible images.' It uses a specific verb and resource, and adds the contextual hint 'This is how you SEE a video's content,' which distinguishes it from video_clip (creating a clip) and video_transcript (text extraction). An agent can immediately understand what this tool does and why it exists alongside media tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains two usage modes: explicit 'timestamps' or uniform sampling via 'count' with optional start/end window. It also clearly states the 12-frame cap and advises making additional calls for more coverage. However, it does not explicitly mention when to prefer this tool over alternatives like video_clip or media_probe, so it lacks an explicit 'when-not-to-use' exclusion. Still, the internal guidance on parameter selection is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears the full burden. It discloses that it does not download videos for URLs and only extracts embedded subtitles for local files, clarifying it is non-destructive and does not perform speech-to-text. However, it does not state what happens when no subtitles exist or how language fallback works, nor mention any rate limits or side effects. These gaps are minor given the output schema exists, but the description could be more explicit about failure modes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured: a one-sentence purpose, a two-clause behavioral distinction, then a clean Arg list. It front-loads the most important information (what it does and how it differs by source type) before parameter details. Every sentence earns its place, with no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with an output schema, the description covers the essential context: purpose, source distinction, parameter semantics, and non-destructive behavior. It does not explicitly cover edge cases like missing subtitles or language fallback, but these are partially implicit. Given the complexity (4 parameters, two modes) and that annotations are absent, it is slightly thin on operational details but still sufficient for correct invocation in most scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully explain parameters. It does: the Args block describes each of the four parameters with examples ('en', 'de', 'ja'), clarifies that max_chars caps output with a default of 20000, and explains that timestamps are prefixed as [MM:SS]. This adds meaning well beyond the schema's titles and defaults, giving the agent precise context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb+resource ('Get a clean text transcript of a video') and immediately distinguishes two source types (URL vs local file) and the mechanisms used (yt-dlp for captions, embedded track extraction). This clearly differentiates it from siblings like video_info, video_frames, and audio_transcribe, 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 Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the two modes of operation (URL vs local file) and notes that it does not download videos, which sets expectations. It does not explicitly list alternatives or exclusion conditions (e.g., when to use audio_transcribe instead), but the differentiation is implicit through the mention of captions/subtitles rather than audio transcription. This provides clear usage context without explicit 'when not to use' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses the conversion operation, support for local paths or URLs, default output directory, and the constraint that bitrate applies only to lossy formats. It also clarifies the meaning of sample_rate=0 as 'keep'. While it doesn't mention overwrite behavior or error conditions, it covers the key behavioral aspects needed for correct invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear one-line summary followed by an Args list. Every sentence adds value; there is no fluff. The core action is front-loaded, and the parameter guidance is compact but complete.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (so return values are already defined elsewhere) and the description covers all parameters, defaults, constraints, and a usage tip, nothing essential is missing for an agent to call the tool correctly. It is a complete specification for this conversion operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates by explaining every parameter: source (path/URL), output_format (enumerates options and default), output_path (default location), bitrate (examples and format constraint), sample_rate (meaning of 0 and use case), and mono (effect). This goes far beyond the bare schema definitions and gives agents actionable guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Convert an audio file to another format', and immediately enumerates the optional transformations (bitrate, sample rate, mono). This clearly distinguishes it from sibling tools like audio_trim, audio_waveform, and audio_transcribe, which have very different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use the tool by defining its core function (format conversion) and providing a practical tip ('16000 mono is ideal for speech-to-text pipelines'). It does not explicitly name alternatives or state when not to use it, but the purpose is so unambiguous that an agent can identify the correct tool from the sibling list without further guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals operation ordering, default output path (~/Downloads/openmedia/), that the result is written to disk, that rotate expands canvas, and that return_preview optionally returns a preview to the model. It does not detail error handling or file overwrite behavior, but the key behavioral traits are covered for a safe default. Slight gap on edge cases but overall strong.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the operation order and key behavior. It uses a clear Args list for parameters, avoiding prose. Each sentence earns its place: the intro states purpose, the next explains order and application, then the Args block covers all parameters with concise definitions. No redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (11 parameters, no schema descriptions, no annotations, no output schema), the description is remarkably complete. It explains every parameter's meaning, defaults, operation order, default output path, and optional preview. The only minor omissions are error handling and file overwrite policy, but these are not essential for a basic agent to invoke the tool correctly. The description provides everything needed for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain all 11 parameters, and it does. Each parameter (source, output_path, resize_width, resize_height, crop, rotate, flip, grayscale, output_format, quality, return_preview) has a clear meaning, including units, defaults, and allowed values (e.g., crop format, flip options). This fully compensates for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool transforms images with a list of operations: crop, resize, rotate, flip, grayscale, and format conversion. It makes the resource (image) and action (transform) clear, distinguishing it from other media tools like audio_convert or video_clip. The operation order is even specified, leaving no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by listing image-specific transformations and noting that only specified operations apply. It doesn't explicitly name alternatives or when not to use it, but sibling tools are clearly for different media (audio/video) or separate purposes (image_info, image_view), making misselection unlikely. Clear context but no explicit exclusions, so a 4 is appropriate.
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 discloses that the image is 'automatically downscaled and recompressed to protect context,' which is a key behavioral trait. It also explains that region is applied before scaling. This is transparent about the main processing steps, though it does not mention error handling, supported formats, or output format. The core behavior is well disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly efficient. The opening sentence states the core purpose, followed by a sentence on the automatic downscaling behavior (key for context), then the region usage hint, and finally a clean Args list. Every sentence earns its place, and the most important details (purpose and scaling) are front-loaded. No redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters, no output schema, and no annotations, the description covers all necessary information: input source type, all parameter details with ranges and defaults, the special region behavior, and the fact that output is model-visible content. The agent can correctly call this tool without needing further clarification. The only minor omission is a note on supported file formats, but that is reasonable to assume.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It does: every parameter (source, max_dimension, quality, region) is explained with purpose, format, ranges, and defaults. For example, region is described as 'x,y,w,h' in original pixel coordinates, and max_dimension has a range (64-4096). This far exceeds what the schema provides and gives the agent complete knowledge for invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'View an image (local path or URL) as model-visible content.' It specifies the verb, resource, and unique behavior (making content visible to the model). This distinguishes it from siblings like image_info (metadata), image_ocr (text extraction), and image_transform (editing) without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use the tool (to view an image) and provides guidance for the 'region' parameter to zoom into details at full resolution. It does not explicitly mention when not to use it or name alternatives, but the clear purpose and sibling context make selection straightforward. The context is clear but lacks explicit exclusions.
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 of behavioral disclosure. It reveals the return value ('Returns the file list'), default output directory, and the min_dimension filtering behavior. It does not mention whether the operation is read-only (obvious for extraction), file overwrite behavior, or error conditions, but the given details are substantive and sufficient for basic usage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficient and well-structured. It opens with the core purpose, then lists parameter explanations in a compact Args block. Every sentence earns its place—no fluff. It is front-loaded with the action and returns a brief note on next steps, making it easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters, an output schema, and a clear workflow, the description covers all necessary aspects: purpose, parameter semantics, expected output, and integration with sibling tools. It omits minor details like error handling or output dir creation, but the output schema provides return structure, and the description gives enough to call the tool correctly without ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain all parameters, and it does thoroughly. It defines source as 'Local file path or direct http(s) URL', pages as page selection like '1-5' with empty meaning whole document, output_dir with a default, and min_dimension with its purpose. This adds clear meaning beyond the bare schema titles and defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action: 'Extract embedded raster images (figures, photos) from a PDF to files on disk.' This uses a precise verb and resource, and the mention of 'raster images' and saving to disk differentiates it from other PDF tools like pdf_read, pdf_view, and pdf_ocr. The follow-up hint to use image_view also helps distinguish its role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides usage context by stating it extracts images and suggests using image_view afterwards for inspection. However, it does not explicitly mention when not to use this tool or compare it with other PDF tools like pdf_info or pdf_ocr. The purpose inherently implies when to use it, but lacks explicit exclusions or alternative selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosure. It honestly states that the tool only retrieves metadata and returns information about the text layer, which implies it is read-only. It also reveals a useful behavioral aspect: distinguishing between embedded text and scanned-only pages. However, it does not mention potential error conditions (e.g., network failures for URLs) or any rate limits, but these are not critical for a metadata query.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured. It leads with the core purpose and metadata list, then provides usage guidance in a single sentence, and finally documents the argument. Every sentence adds value, and there is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Since the tool has an output schema (per context signals), the description does not need to list return values. It adequately covers the tool's function, usage context, and parameter. The only minor gap is the absence of edge-case handling (e.g., what happens with invalid files or URLs), but for a simple metadata tool with one parameter, this is acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does 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 parameter, and it does: 'source: Local file path or direct http(s) URL to a PDF.' This adds concrete semantics (accepted formats, supported sources) beyond the plain string type in the schema, fully compensating for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Get') and a clear resource ('PDF metadata'), then enumerates the exact metadata fields (page count, title, author, dates, encryption, page size, file size, text layer presence). It also distinguishes itself from sibling tools by explicitly mentioning the decision between pdf_read, pdf_view, and pdf_ocr, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this first to decide between pdf_read (text layer) and pdf_view/pdf_ocr (scanned).' This gives clear when-to-use guidance and names the alternative tools and the condition that routes to each. It leaves no ambiguity about when this tool should be called.
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 the full burden of behavioral disclosure. It explains the default stream copy vs reencode tradeoff, keyframe snapping, default output path, and URL resolution (≤720p). This fully discloses mutation and side effects, so an agent understands what happens when calling it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is logically structured with a one-line summary followed by a compact Args list. Every line adds value, though it could be tightened slightly by merging some details (e.g., reencode and keyframe note) without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers all necessary call semantics: input source handling, time formats, output path, reencode behavior, and defaults. The presence of an output schema means return details need not be explained. Nothing an agent needs to call this tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The Args section explicitly documents all five parameters, including their types (e.g., seconds or HH:MM:SS for start/end) and default behaviors (e.g., output_path default). Schema description coverage is 0%, but the tool description compensates completely, adding meaning beyond the plain property names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('cut'), resource ('video'), and result ('new file'), which clearly differentiates it from siblings like video_info or video_frames. There is no ambiguity about what the tool accomplishes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The described purpose and argument details make the context of use obvious (creating clip excerpts), though it doesn't explicitly name sibling alternatives or state when not to use it. This is clear enough for an agent to decide when to invoke it, but lacks explicit routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the tool operates on the text layer, mentions performance ('Fast and cheap'), and discloses the default character cap (20000) and the layout-preservation option. It does not detail error handling or potential side effects (e.g., encrypted PDFs), but for a read operation this is adequate and adds meaningful context beyond the tool name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: the core purpose and usage-scoping appear in the first sentence, followed by a clear exclusion for scanned PDFs and a structured Args list. Every sentence earns its place without fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists (return structure is presumably covered there), the description provides all necessary information for correct invocation: what it does, when to use it, how to select pages, cap results, and preserve layout. The sibling-tool context and the explicit routing to pdf_info/pdf_ocr/pdf_view make it self-contained and complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the per-parameter explanations in the description are vital. Each parameter (source, pages, max_chars, preserve_layout) is fully explained with examples, defaults, and effects, which goes far beyond the bare schema titles. This fully compensates for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb-resource pair ('Extract text from a PDF's text layer') and immediately scopes it to born-digital PDFs. It explicitly distinguishes from sibling tools by naming pdf_ocr and pdf_view for scanned PDFs, so the agent can tell them apart without opening their schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It states exactly when to use this tool ('born-digital PDFs') and when not ('scanned PDFs ... use pdf_ocr or pdf_view'), and even tells the agent to check with pdf_info first. This is explicit routing guidance with exclusions and alternatives, leaving no room for misinterpretation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Builderstar/openmedia-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server