scholar-mcp

Resolve Standard Identifier

resolve_standard_identifier

Read-only

Normalize a messy citation string into its canonical form, using local patterns first and falling back to source APIs, returning all candidates when ambiguous.

Instructions

Normalise a messy standard citation string to its canonical form.

Tries local regex first (fast, no network). Falls back to querying source APIs when local patterns don't match. Returns all candidates when the input is ambiguous.

A cold catalogue makes this slow: the first call after a fresh install or a cleared cache downloads and parses each body's index, which runs well past the soft deadline. Such a call returns a job handle to poll with get_job_result rather than the result itself.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`raw`	Yes	Raw citation string as it appears in a paper reference.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedv1.10.0Sep 8, 2026
- addedOutput schema / additionalProperties
```
Added value: +true
```
- removedOutput schema / properties
```
Removed value: -{
-  "result": {
-    "type": "string"
-  }
-}
```
- removedOutput schema / required
```
Removed value: -[
-  "result"
-]
```
- removedOutput schema / x-fastmcp-wrap-result
```
Removed value: -true
```
First observedv1.9.0Aug 10, 2026

TDQS

A4/5.0

Behavior4/5

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

Beyond the annotations (readOnlyHint, destructiveHint), the description discloses important behavioral nuances: network fallback, cache-related performance degradation, and the possibility of returning a job handle (get_job_result) instead of the result. This adds significant context not captured by annotations.

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?

The description is well-structured and concise, with each sentence adding distinct value: purpose, algorithm, ambiguity handling, and performance caveat. No redundant or filler content.

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

Completeness4/5

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

Given the tool's complexity (fallback logic, cache behavior, job polling), the description provides sufficient context for an agent to understand when to expect a direct result versus a job handle. It does not specify error conditions, but the output schema likely covers return structure. Overall, well-rounded for the complexity level.

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?

The tool description does not add additional parameter information beyond the schema, which already provides a description for 'raw' (coverage 100%). The phrase 'messy standard citation string' loosely echoes the schema but does not expand on format, constraints, or examples. Baseline for full coverage is 3.

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 clearly states the tool's purpose: 'Normalise a messy standard citation string to its canonical form.' It uses a specific verb (Normalise) and resource (standard citation string), and further clarifies behavior with 'Returns all candidates when the input is ambiguous.' This distinguishes it from siblings like search_standards or get_standard.

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

Usage Guidelines3/5

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

The description gives some behavioral guidance (local regex first, fallback to APIs) but does not explicitly state when to use this tool over alternatives like batch_resolve or search_standards. It mentions the cold cache scenario and job handle, but lacks direct 'use this when...' or 'instead of...' instructions.

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