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Santismm Knowledge — Harness Engineering, Agentic AI & Governance

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Read-onlyIdempotent

Ranked keyword search across the whole corpus (knowledge, patterns, architectures, governance and the handbook). Matches every language and ignores accents, so query in the user's own words. Each hit carries a relevance score and the fields it matched; follow up with the matching get_* tool for full detail. Use this before any get_* tool whenever you have a question rather than an identifier.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 20).
queryYesKeyword or phrase to search for in any supported language.
localeNoLanguage of the returned body. Default: en.
domainsNoRestrict to these domains. Omit to search everything, including the handbook.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
queryYes
resultsYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Even with annotations already indicating read-only, idempotent, and non-destructive behavior, the description adds valuable behavioral detail: ranked results, accent-insensitive matching, multi-language support, and per-hit relevance scores plus matched fields. This goes well beyond the structured annotations and provides actionable expectations.

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

Conciseness5/5

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

Four sentences, each earning its place: scope, language/accent behavior, result details with follow-up instruction, and usage routing. The tool's purpose is front-loaded and there is no filler or repetition of schema content.

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

Completeness5/5

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

The annotations cover safety semantics, the schema covers all parameters, and an output schema exists. The description supplies the remaining operational context: when to invoke it, what results look like, and how to retrieve full details. Nothing critical is missing for an agent to call it effectively.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful query-construction guidance beyond the schema by saying to query in the user's own words and noting that accents are ignored. It does not elaborate on limit, locale, or domains, but those are already well documented in the schema.

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

Purpose4/5

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

The description clearly states a specific verb and resource: 'ranked keyword search across the whole corpus' with domain examples. It is unambiguous about what the tool does, but it does not explicitly differentiate itself from the sibling tools 'search_all' and 'search_articles', so it stops short of a 5.

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

Usage Guidelines4/5

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

The description gives strong when-to-use guidance: 'Use this before any `get_*` tool whenever you have a question rather than an identifier.' It also tells the agent to follow up with the matching get_* tool. However, it does not mention when not to use this tool or point to alternatives like search_all or search_articles.

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

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TDQS

A4.1/5.0
Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness4/5

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.