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everlog-mcp

everlog-mcp

by jlconlin
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

search_entries

Read-onlyIdempotent

Search and browse journal entries by full-text query, journal, tag, date range, or bookmarked status, returning match counts and paginated snippets for review.

Instructions

Search and browse journal entries. Returns total match count and a page of results, each with entry_id, date, journal, title, tags, word_count and a snippet (matches wrapped in «»).

query: full-text (SQLite FTS5): words are ANDed; "exact phrase"; prefix*; OR; NOT; stemmed. Invalid syntax falls back to matching all words. Omit to filter without text search. journal: journal name (case-insensitive). tag: tag path; also matches child tags. start_date / end_date: inclusive YYYY-MM-DD bounds on the entry's local day. sort: relevance (default with query) | newest (default without) | oldest. limit: 1-100 (default 20); offset for paging. Fetch full text with get_entry.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "search_entries"
  }
}

Input Schema

NameRequiredDescriptionDefault
tagNo
sortNo
limitNo
queryNo
offsetNo
journalNo
end_dateNo
bookmarkedNo
start_dateNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds substantial behavior beyond them: return shape, snippet highlighting with «», and a documented fallback where invalid FTS5 syntax degrades to matching all words. Minor gap: nothing about how paging interacts with a large result set or performance limits.

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?

Front-loaded with purpose and return shape, then a tight per-parameter block. Every line adds information an agent needs to build a correct call; nothing is redundant with the schema.

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?

For a 9-parameter search tool with an output schema, this is essentially complete: query grammar, filter semantics, sort defaults, paging, and the escalation path to get_entry are all covered. The lone hole, the bookmarked filter, is a small residual against an otherwise thorough definition.

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 0%, so the description carries the full burden and mostly does: it explains FTS5 query syntax (AND, phrase, prefix*, OR, NOT, stemming), case-insensitive journal matching, tag child matching, inclusive YYYY-MM-DD local-day bounds, and the 1-100 limit range with default 20. The only undocumented parameter is bookmarked, which appears in the schema with no explanation anywhere.

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

Purpose5/5

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

Opens with a specific verb+resource ("Search and browse journal entries") and immediately states what comes back: total match count plus a page of results with named fields. It also distinguishes itself from get_entry by pointing there for full text, so an agent can pick between the two without opening either schema.

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

Usage Guidelines5/5

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

Gives explicit routing rules: omit query to filter without text search, and use get_entry to fetch full text. It also explains conditional defaults for sort (relevance with a query, newest without), which is exactly the kind of when-to-use guidance that prevents a wrong call.

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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