markdown_toc
Build a table of contents from markdown headings. When: Build heading TOC from markdown docs.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| max_level | No |
Build a table of contents from markdown headings. When: Build heading TOC from markdown docs.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| max_level | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavioral traits. It does not disclose how headings are extracted (e.g., only '#' style?), the output format, or handling of edge cases. The max_level parameter is not explained. The description is too minimal.
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 short, but the second sentence largely repeats the first. It is front-loaded with the core purpose. Could be slightly more concise by merging.
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 no output schema, the description should indicate the return format (e.g., a string containing markdown list). It does not address this, nor does it mention the structure of the TOC (e.g., nested list with links?). The tool is simple but still missing essential context.
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 should explain parameters. It does not describe the 'text' parameter (must be markdown content) or the 'max_level' parameter (controls heading depth). The description adds no semantic value beyond the 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 it builds a table of contents from markdown headings, specifying the verb and resource. It distinguishes itself from siblings like markdown_to_html and markdown_link_extract by focusing specifically on TOC generation, though it doesn't explicitly name them.
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 phrase 'When: Build heading TOC from markdown docs' gives a basic usage context but lacks guidance on when not to use this tool or alternatives. It implies a specific scenario but offers no exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Every tool has a clear, distinct purpose with thorough descriptions. Even closely related tools like base64_decode/encode and hash_md5/sha256 are easily differentiated by name and description.
All tools follow a consistent lowercase_underscore naming convention, typically in a <domain>_<action> or <action>_<domain> pattern. There are no jarring deviations or mixed styles.
193 tools is an extreme count, far beyond what any focused server needs. While each tool has utility, the sheer number creates a kitchen-sink effect that overwhelms agents and hinders discoverability.
Within each subdomain (JSON, cron, JWT, etc.), the coverage is exhaustive, covering validation, conversion, parsing, and more. Minor gaps exist (e.g., YAML-to-TOML conversion missing), but overall it is remarkably complete.