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Spelling Suggestion

eutils_espell
Read-onlyIdempotent

Check spelling of a query against an NCBI Entrez database and retrieve the suggested correction. Use when a search returns no results due to a suspected typo.

Instructions

Get NCBI's spelling suggestion for a query in one database.

Args:

  • db (string): database to check against, for example "pubmed".

  • term (string): query to check, for example "breast cancr".

  • response_format ('markdown' | 'json'): output format. Default 'markdown'.

Returns: { database, query, corrected_query, changed }

Examples:

  • Use when: a search returned no results and you suspect a typo

  • Use when: "did you mean" for a query -> term="diabetis"

  • Don't use when: the query is a structured field search; ESpell works on plain terms

Error Handling:

  • Reports changed=false when NCBI has no correction, rather than an error

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dbYesEntrez database to check the spelling against, for example "pubmed".
termYesQuery whose spelling should be checked, for example "breast cancr".
response_formatNoOutput format: 'markdown' for human-readable text, or 'json' for machine-readable data. Default: 'markdown'.markdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
changedYes
databaseYes
corrected_queryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so safety is covered. The description adds genuinely useful context beyond that: the error-handling note that changed=false is reported rather than an error when NCBI has no correction. It stops short of describing rate limits or response latency, but the error semantics are a real value-add.

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

Conciseness4/5

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

Well front-loaded with purpose first, then structured Args/Returns/Examples/Error Handling sections. Every section earns its place except the Args block, which duplicates the schema verbatim, making it slightly longer than necessary.

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?

Although an output schema exists (so return explanation is optional), the description still documents the return shape and even covers the no-correction edge case. For a 3-parameter, fully-annotated tool, an agent has everything needed to select and invoke it correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so the Args section largely restates what the schema already documents for db, term, and response_format. The only marginal addition is the illustrative example term 'breast cancr', which does not deepen syntactic understanding. Baseline 3 applies when the schema does the heavy lifting.

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?

The description opens with a specific verb and resource: 'Get NCBI's spelling suggestion for a query in one database.' An agent can immediately distinguish this diagnostic spelling tool from search/fetch siblings like eutils_esearch or eutils_efetch.

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?

Explicit 'Use when' bullets (no-results search, 'did you mean' scenario) and a 'Don't use when' exclusion for structured field searches. The alternative condition is stated precisely, so nothing is left to inference.

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