list_knowledgeList all knowledge units (concepts on agentic & enterprise AI) with slug, category, title, summary and Evidence-First provenance. Use this to browse the domain; use search when you have a question rather than a slug.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | Language of the returned body. Default: en. |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | ||
| results | Yes |
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?
Annotations already cover readOnly, idempotent, and non-destructive behavior. The description adds useful context beyond annotations: it lists all units, includes Evidence-First provenance, and frames the operation as a domain browse. No contradiction with annotations.
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?
Two dense sentences with no filler. The first sentence states purpose and output shape; the second provides usage routing. Every word earns its place.
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, zero-required-parameter read-only listing tool with an output schema and rich annotations, the description is complete. It tells the agent what it returns, when to use it, and when to use the search alternative.
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 sole parameter (locale) is fully documented in the schema, including allowed values and default, so the description need not repeat it. The description adds no parameter-specific semantics, but with 100% schema coverage, the baseline of 3 is appropriate.
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 a specific verb ('List') and resource ('knowledge units'), defines the domain scope ('agentic & enterprise AI'), and enumerates the returned fields. It clearly differs from search and other list_* siblings by naming the exact resource.
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?
Explicitly states when to use this tool ('Use this to browse the domain') and names the alternative for question-driven lookups ('use `search` when you have a question rather than a slug'). This is clear routing guidance.
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.
Most tools are cleanly separated by content type and the list_/get_ pairs are predictable. The main ambiguity is among search, search_all, and search_articles: search claims to cover the 'whole corpus' while search_all actually expands to essays, labs, claims, and the Homeric Atlas, so an agent could select the narrower search and miss content.
Every tool follows the same snake_case verb_noun pattern: calculate_*, get_*, list_*, and search_*. Even the three search variants are predictable from their suffixes, so there are no mixed naming conventions.
30 tools is above the preferred MCP size and creates real selection burden for agents, even though the multi-surface knowledge scope explains the volume. The set is systematic rather than bloated, but 25+ tools is still too many for a typical server surface.
Each content surface has browse, fetch, and search coverage, and get_related plus get_overview provide cross-cutting navigation. The only meaningful gap is that the relationship between search and search_all is not fully disjoint, which can create a dead-end if the wrong search tool is chosen first.