Retrieve a full knowledge entry by domain and slug.
Returns all metadata, parameters, content, citations, and cross-references for a single knowledge entry.
Args: domain: The engineering domain (e.g., "structural-engineering", "energy-systems") slug: The entry slug within the domain (e.g., "superstructure/primary-geometry")
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| domain | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 carries the transparency burden. It clearly discloses the read-only nature ('Retrieve', 'Returns') and enumerates the data payload, providing sufficient behavioral context for a retrieval tool. It does not detail error handling or authorization, but this is not critical for a simple read.
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: a front-loaded purpose statement, a brief line on return contents, and a clear Args section. Every sentence adds value, with no redundancy 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?
The tool is simple (two parameters, no nested objects) and an output schema exists, so the description does not need to elaborate on return format. It covers purpose, parameters, and return contents sufficiently for agent selection and invocation.
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 provides only string types with zero description coverage. The description compensates by giving meaningful semantics for both 'domain' and 'slug', including the role of each parameter and concrete examples (e.g., 'structural-engineering', 'superstructure/primary-geometry').
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 retrieves a full knowledge entry by domain and slug, listing the specific content types returned (metadata, parameters, content, citations, cross-references). This distinguishes it from sibling tools that retrieve only subsets (e.g., get_entry_parameters, get_cross_references).
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 a complete knowledge entry – but does not explicitly state when to use this tool vs alternatives like get_entry_parameters or search_knowledge. No exclusions or alternative tools are mentioned, so guidance is only implicit.
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.
Each tool has a distinct purpose: retrieving entries, cross-references, parameters, stats, open questions, domains, search, registration, and submission. No two tools appear to perform the same function, and the descriptions clarify when to use each.
All tool names follow a consistent verb_noun pattern with snake_case: get_cross_references, list_domains, read_node, submit_proposal, etc. The verb prefixes (get, list, read, search, register, submit) align with their operations, making the pattern predictable.
With 9 tools, the set is well-scoped for a knowledge base system. Each tool covers a core functionality (retrieval, search, stats, submission, registration) without redundancy or bloat, striking the right balance for the stated purpose.
The tool surface covers the main workflows: reading, searching, cross-referencing, statistics, and submission. Minor gaps exist, such as no direct listing of all entries in a domain and no update/delete operations for submissions, but these are workable around and do not severely hinder typical usage.