MCP Document Reader

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions the tool extracts images and returns JSON metadata, but lacks critical behavioral details: whether it modifies the original file, handles errors (e.g., invalid paths), requires specific permissions, or has performance constraints. For a file operation tool with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with three sentences: purpose, parameters, and return value. It's front-loaded with the core functionality. The parameter and return explanations are necessary given the lack of schema descriptions, though the structure could be slightly more polished (e.g., avoiding markdown-like syntax).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (file processing with two parameters), no annotations, and an output schema present (which handles return values), the description is minimally adequate. It covers purpose and parameters but lacks behavioral context like error handling or side effects. With output schema reducing the need to explain returns, a score of 3 reflects this partial completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explicitly documents both parameters: 'filename' as the path to the DOCX document and 'output_dir' as an optional directory for saving images. This adds clear meaning beyond the schema's generic titles. However, it doesn't detail parameter formats (e.g., absolute vs. relative paths) or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Extracts embedded images'), target resource ('from a DOCX file'), and output format ('returns structured JSON metadata'). It distinguishes itself from sibling tools like read_document, write_presentation, and write_word_document by focusing on image extraction rather than document reading or writing operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., file must exist, DOCX format required), compare with similar tools, or indicate scenarios where extraction might fail. The agent must infer usage from the purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions supported file formats and the return type ('Extracted text'), but lacks details on error handling (e.g., unsupported formats, file not found), performance (e.g., large file handling), or permissions required. For a read operation with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by a concise list of supported formats and parameter details. Every sentence adds value without redundancy, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (single parameter, read-only operation) and the presence of an output schema (which handles return values), the description is mostly complete. It covers purpose, supported formats, and parameter semantics, but could improve by adding behavioral details like error handling or limitations, especially since no annotations are provided.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that the 'filename' parameter is a 'Path to the document file to read (supports absolute or relative paths)', clarifying usage and format. With only one parameter, this compensates well 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.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Reads and extracts text from a specified document file.' It specifies the verb ('Reads and extracts'), resource ('document file'), and scope ('text'), but does not explicitly differentiate from sibling tools like 'extract_document_images' beyond the focus on text versus images.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It lists supported file formats but does not mention when to choose this over 'extract_document_images' for image extraction or other siblings for writing operations. Usage context is implied by the tool's name but not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool generates a presentation and describes the return value, but lacks critical details such as permissions required, file system impacts, error handling, or rate limits. For a write operation with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, starting with the core functionality. Each sentence adds value, such as format details and parameter explanations, with no wasted text. The structure is clear, though it could be slightly more streamlined by integrating parameter details more cohesively.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a presentation generation tool with 4 parameters, 0% schema coverage, and no annotations, the description is moderately complete. It covers the basic operation and parameters but lacks depth in behavioral aspects and usage context. The presence of an output schema helps by documenting the return value, but overall completeness is adequate with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining each parameter's purpose (e.g., 'Target output path ending with .pptx or .ppt' for filename, 'Optional title slide title' for title). However, it does not fully detail the structure of 'slides' (e.g., what 'slide definitions' entail) or provide examples, leaving some ambiguity. This partial compensation justifies a baseline score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'Generates a PowerPoint presentation in PPTX format, or PPT via LibreOffice conversion,' which specifies the verb (generates) and resource (PowerPoint presentation). It distinguishes from siblings like write_word_document by specifying the output format, though it could be more explicit about the distinction. It's not tautological and provides a clear purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like write_word_document or other siblings. It mentions the output formats but does not specify scenarios, prerequisites, or exclusions for usage. This leaves the agent without contextual direction for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool generates documents and returns a JSON payload, but lacks details on permissions, error handling, rate limits, or side effects. For a write operation with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, starting with the core purpose. Each sentence adds value: format details, parameter explanations, and return information. There's minimal waste, though the parameter list could be more integrated into the narrative flow.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters, write operation) and no annotations, the description covers purpose, parameters, and return value. With an output schema present, it doesn't need to explain return values in detail. It's mostly complete but could improve on behavioral context and usage guidelines.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It lists all four parameters with brief explanations (e.g., 'Target output path ending with .docx or .doc'), adding meaning beyond the schema. However, it doesn't fully detail parameter constraints or formats, leaving gaps like table structure specifics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generates a Word document in DOCX format, or DOC via LibreOffice conversion.' It specifies the verb ('Generates'), resource ('Word document'), and format details, distinguishing it from sibling tools like extract_document_images (extraction), read_document (reading), and write_presentation (different document type).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like write_presentation for presentations or read_document for reading documents, nor does it specify prerequisites or contexts for choosing this tool. Usage is implied but not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.