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omitly

omitly-mcp

Official
by omitly

locate_text

Find exact text strings in a PDF and get page coordinates for each occurrence. Use for names, addresses, or account references that pattern matching misses, then pass the returned regions to redact.

Instructions

Locate exact text strings in a PDF and return each occurrence's page and coordinates (in PDF points). Use this for what pattern-matching can't catch — names, addresses, account references — by doing the entity recognition YOURSELF and passing the literal strings here; the engine resolves where they sit so you never guess geometry from a rendered page. Feed the returned regions straight to redact_pdf. Case-insensitive; a string the PDF splits across text operators may not match as one run. Each hit returns a masked preview, not the raw text. Nothing is uploaded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textsYesliteral strings to locate
pdfPathYesabsolute path to the PDF to search

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
regionsYes
Behavior5/5

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

With no annotations provided, the description fully carries the behavioral disclosure burden. It discloses case-insensitivity, the split-text-operator limitation, the masked-preview output ('not the raw text'), and the privacy guarantee ('Nothing is uploaded'). This is exemplary transparency beyond what any schema could convey.

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 front-loaded with the core purpose and each sentence adds distinct value: purpose, usage guidance, behavioral caveats, output format, and privacy. It is appropriately sized for the tool's complexity without redundancy or filler.

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 description is fully self-contained for a two-parameter tool. It explains what the tool returns, how to use it effectively, important edge-case caveats, and privacy behavior. An output schema exists to describe the return structure, so the description does not need to list return fields.

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 input schema already describes both parameters completely (pdfPath and texts with descriptions), so the baseline is 3. The description reinforces that texts are 'literal strings' and that matching is exact, but it does not add substantial new parameter-level detail. The schema does the heavy lifting here.

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: 'Locate exact text strings in a PDF and return each occurrence's page and coordinates (in PDF points).' It clearly distinguishes from siblings by framing it as the exact-match counterpart to pattern-matching tools. This makes the tool's purpose immediately unambiguous.

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 guidance is given: 'Use this for what pattern-matching can't catch' and the user is told to do entity recognition themselves and pass literal strings. It also names the downstream workflow: 'Feed the returned regions straight to redact_pdf.' This provides clear when-to-use context and an implicit alternative (pattern-matching).

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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