inktomd MCP Server

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

With no annotations, the description carries the full burden. It discloses the output format, preservation of headings/sections, and the token efficiency benefit, which is meaningful behavioral detail. It could mention limitations like paywalled content, but the core behavior is well covered.

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 three focused sentences with no redundant phrasing. Each sentence adds value: what it does, supported inputs, and output/token benefit.

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?

For a single-parameter conversion tool, the description is complete: it covers the input type, output format, and use case. The presence of an output schema further reduces the need to describe return values in detail.

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 coverage is 0% for the single 'url' parameter, but the description fully compensates by detailing both accepted URL patterns (arxiv.org/abs/PAPER_ID and arxiv.org/pdf/PAPER_ID). This is exactly the semantic guidance needed beyond the bare property name.

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 converts ArXiv research papers to structured Markdown, using a specific verb and resource. It distinguishes itself from sibling conversion tools by explicitly targeting ArXiv URLs.

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 explicitly lists the accepted URL formats (abstract pages and PDF links), which tells the agent exactly when this tool is appropriate. It does not explicitly exclude other cases, but the ArXiv focus provides clear usage context.

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, the description carries the full burden. It discloses batch behavior, independence of each conversion, URL format constraints, and the return structure. Minor gaps remain around error handling or partial failures, but overall it provides strong behavioral context.

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

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

The description is concise and well-structured: a one-sentence overview followed by clear Args and Returns sections. Every sentence adds necessary information without fluff or redundancy.

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 a simple one-parameter tool and an output schema, the description covers the key aspects: input constraints, independent processing, and return format. It doesn't discuss error behavior or relationship to siblings in depth, but these are secondary for a straightforward batch converter.

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 schema has one parameter with 0% coverage, but the description's Args section fully compensates by explaining that 'urls' is a list, max 10, and must start with http/https. This adds meaning beyond the bare schema definition.

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 'Convert multiple URLs to Markdown in a single call,' specifying the verb, resource, and format. This distinguishes it from siblings like convert_url (single URL) and convert_youtube, making the tool's purpose immediately obvious.

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 implies usage for batch conversion with 'multiple URLs' and provides constraints (max 10, URL must start with http/https). However, it doesn't explicitly contrast with alternatives like convert_url or mention when not to use it, falling short of explicit when/when-not guidance.

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 reveals essential traits: conversion to Markdown, a comprehensive list of supported formats, the requirement for an absolute path, and a maximum file size. While it doesn't detail error handling or edge cases, it provides enough practical behavioral context for an agent to select and invoke the tool safely.

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 a single, focused paragraph that front-loads the primary purpose, then delivers format support, a path requirement, and size limit—all in compact, useful sentences. No wasted words, and the structure is easy to scan.

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?

The tool supports many formats and has an output schema present, so the description needn't detail return values. It covers the key operational aspects: what it converts to, which inputs are supported, the path format, and a size constraint. It could mention error behavior or archive handling nuances, but overall it is complete enough for an agent to use effectively.

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 schema has only one parameter (file_path) with 0% description coverage, so the description must compensate. It does so by specifying 'Provide the absolute file path' and listing supported formats, which effectively constrains what the parameter should contain. This adds meaningful guidance beyond the raw schema.

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 verb 'Convert' and the resource 'a local file to clean AI-ready Markdown'. It explicitly lists supported formats, which distinguishes it from sibling tools like convert_url, convert_arxiv, and convert_youtube that handle remote sources. The purpose is unambiguous and specific.

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 clear context: use this tool for local files, and the supported format list implies when it should not be used (unsupported types). It instructs users to 'Provide the absolute file path' and notes the 20MB limit. It does not explicitly name alternatives like 'use convert_url for online content', but the local-file emphasis and sibling context make the usage clear.

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 burden of behavioral disclosure. It does reveal key behavioral traits: output is Markdown and it reduces tokens by up to 63%. However, it does not mention potential failure modes (e.g., unsupported URLs, paywalls, authentication), rate limits, or whether content is fetched server-side. This is moderate transparency—enough to understand the core behavior but missing edge-case context.

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 two sentences long and front-loaded with the core action. The first sentence states the purpose, the second lists supported inputs and a benefit. No redundant details or filler, making it highly concise and well-structured.

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?

For a tool with many supported input types and no annotations, the description lacks essential edge-case coverage: what happens for invalid/unsupported URLs, whether authentication or private content is handled, or any limits on file size/request frequency. While the listed sources and token reduction give a solid overview, the absence of error-handling and constraints leaves gaps. The presence of an output schema reduces the need to explain return values, but behavioral context is incomplete.

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 coverage is 0% (the only parameter 'url' has no schema description). The description partially compensates by explaining that the URL can be any supported type and by listing examples. However, it does not add specific technical constraints (e.g., URL formats, encoded requirements). Given the single obvious parameter, the minimal added meaning is acceptable, scoring a 3.

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 a specific verb ('Convert'), resource ('any URL'), and outcome ('to clean AI-ready Markdown'). It distinguishes from siblings like convert_arxiv and convert_youtube by positioning itself as the general-purpose converter, listing a wide range of supported sources. This makes the tool's scope immediately clear.

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 clear context: use this for any URL, with a broad list of supported types including webpages, YouTube, ArXiv, etc. It does not explicitly mention when to prefer specialized siblings like convert_arxiv, but the 'any URL' framing implies this is the default general tool. There are no exclusions stated, which aligns with a 4 (clear context, no exclusions) rather than a 5 (explicit when/when-not/alternatives).

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. It transparently describes the return format, including a metadata header with token counts, word count, character count, and reading time. However, it does not disclose potential side effects (e.g., network calls for URLs) or failure conditions, which is a minor 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 well-structured with Args and Returns sections, each sentence providing essential information without redundancy. It is appropriately concise 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 simple tool complexity (2 parameters) and the existence of an output schema, the description covers input semantics, output format, and key metadata details. It is complete for an agent to select and invoke the tool correctly.

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 fully explains both parameters beyond the input schema. 'source' is clarified as either a URL starting with http/https or an absolute file path, and 'source_type' is specified with exact allowed values and its default. This compensates for the 0% schema description coverage.

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 a specific verb ('convert') and resource ('file or URL') with a defined outcome ('Markdown and structured metadata'). This differentiates it from sibling tools by emphasizing the metadata aspect, which is unique.

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 implies usage for cases where both content and metadata are needed but does not explicitly name alternatives or when not to use this tool. Siblings like convert_url and convert_file are not mentioned, so guidance is implicit rather than explicit.

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 explains input constraints (captions must be enabled), output format (flowing Markdown paragraphs, not raw fragments), and link compatibility. It does not cover failure modes or authentication, but it does not contradict any annotations and adds substantial context.

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 concise and front-loaded with the primary purpose. Each of the four sentences adds specific value: purpose, link formats, caption prerequisite, and output style. No unnecessary words or fluff.

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?

For a simple single-parameter tool with an output schema, the description adequately covers input requirements and return format. It could mention what happens when captions are missing, but the prerequisite is clearly stated, and the output schema likely covers the return structure.

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 input schema only defines 'url' as a required string with no description. The tool description adds meaning by explaining the expected URL formats (youtube.com/watch?v= and youtu.be/) and the public/caption requirements, thus enriching the semantics beyond the bare schema.

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: extract the full transcript from a YouTube video as Markdown. It uses a specific verb and resource, distinguishes itself from siblings like convert_url and convert_file by focusing exclusively on YouTube transcripts, and mentions acceptable link formats.

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 clear context on when to use the tool by specifying the video link types (watch and short links) and the caption requirement. It does not explicitly name alternatives or exclusions, but the YouTube-specific scope effectively differentiates it from the generic convert_url and other conversion tools.

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 burden of behavioral disclosure. It states that OpenAI models use tiktoken and others are estimates, and it details the return contents (count, context window, fit status). This goes beyond the schema and gives the agent useful expectations about accuracy and output.

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 efficiently structured: a one-sentence summary, a brief method note, and clear Args/Returns sections. Every sentence adds necessary information, and there is no repetition or filler, making it easy to parse quickly.

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?

For a simple 2-parameter tool with an output schema, the description is complete. It covers the tool's purpose, the estimation method, all model options with defaults, and the nature of the return value. No critical gaps remain for an agent to invoke the tool correctly.

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 coverage is 0% (the schema provides only types and a default), but the Args section fully describes both parameters. It explains what 'text' is, lists all 10 supported model options, specifies the default model, and clarifies behavior for each, fully compensating for missing schema 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 opens with 'Count the exact number of tokens in a text string for a specific AI model', clearly identifying the verb (count) and resource (tokens for a specific model). It is distinct from the sibling conversion tools (e.g., convert_file, convert_url), which focus on format transformation, making purpose unambiguous.

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's clear focus on token counting makes the appropriate usage context obvious—an agent can immediately determine when token counting is needed. However, it does not explicitly state when not to use the tool or name alternatives, so it misses the top score for explicit exclusions.

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 transparency burden. It clearly states the tool lists supported formats and URL types, implying a read-only operation, and adds context that it is for pre-conversion checking. It does not detail return format, but an output schema exists, so that is not required.

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 two sentences, with the first sentence stating the core function and the second providing practical usage. Every word earns its place; no redundancy.

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?

This is a simple list/discovery tool with no parameters and an output schema. The description fully covers what it lists, why to use it, and how it fits with the conversion workflow, making it complete for its complexity.

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 tool has zero parameters, and the empty schema provides full coverage. Per the rubric, a baseline of 4 applies when there are no parameters, and the description adds no confusing parameter information.

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 uses the specific verb 'list' and clearly identifies the resource as 'all file formats and URL types that inktomd supports for conversion to Markdown.' This distinguishes it from sibling conversion tools like convert_file and convert_url, which perform conversions rather than listing supported inputs.

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 explicitly says 'Use this to check whether a specific file type or URL source is supported before attempting conversion,' which provides clear when-to-use context. It does not explicitly state when not to use it, but the pre-conversion check role is well implied.

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. It discloses the two-step process (convert to Markdown, chunk) and the return structure, along with defaults. Yet it does not mention potential side effects like network requests for URLs, supported input formats, error handling, or any limitations, leaving gaps 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 well-structured: a concise summary sentence, followed by a clear Args list with bullet-like formatting, and a Returns section. Every sentence contributes information, with no redundancy or fluff, making it easy to scan.

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?

The tool is relatively simple, and the description covers the core workflow: conversion, chunking, and output format. The return schema is also described. Minor omissions like supported file formats are likely covered by the sibling tool list_supported_formats, so the description is largely complete for the intended use.

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 schema provides only parameter names, types, and defaults with no descriptions. The description adds significant meaning: source format constraints (URL or absolute path), source_type allowed values, chunk_size recommended range (256-1024), and overlap purpose. This fully compensates for the empty schema and enhances usability.

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 that the tool converts a file or URL to Markdown and then splits it into chunks for RAG pipelines. It specifies the output format (JSON array of chunks) and distinguishes itself from sibling conversion tools by performing both conversion and chunking, positioning it as the 'single tool needed'.

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 communicates that this is the complete tool for going from raw document to RAG-ready data, implying its use when both conversion and chunking are needed. However, it does not explicitly mention alternatives or when not to use it, so it falls short of fully explicit usage guidance.

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