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ZengZichao

SwissBioPics MCP Server

by ZengZichao

get_subcellular_locations

Extract all subcellular locations from a cell type's SVG, returning SL IDs, names, and descriptions for cellular structure analysis.

Instructions

Extract all subcellular locations defined in a cell type's SVG.

Each location includes an SL identifier (e.g. SL0173), a human-readable name (e.g. Mitochondrion), and a detailed description.

Args: cell_identifier: Numeric cell ID (e.g. 8719), name path (e.g. Animal_cell), or SVG file name (e.g. Animal_cells.svg).

Returns: A formatted list of all subcellular locations with SL IDs, names, and truncated descriptions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cell_identifierYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose useful behavior — output is a formatted list and descriptions are truncated — and the read-only nature is self-evident from 'Extract' plus the view-only sibling set. It says nothing about error handling for an unresolvable identifier or any limits on the result set, so it falls short of a full behavioral picture.

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?

The core purpose is front-loaded in the first sentence, and the Args/Returns blocks are compact and skimmable. The Google-style docstring framing is slightly heavier than needed, but nothing is padding.

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?

With an output schema present, the description need not detail return structure, and it still usefully notes the truncated descriptions. Input formats are fully covered. The remaining gap is the absence of any sibling routing or failure-mode note for an invalid cell identifier.

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% for the single parameter, so the description must compensate, and it does: it spells out three accepted identifier forms (numeric ID like 8719, name path like Animal_cell, or SVG file name like Animal_cells.svg) with concrete examples. It stops short of saying how precedence or ambiguity between forms is resolved.

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 names a specific verb and resource — extracting all subcellular locations defined in a given cell type's SVG — and enumerates the fields returned (SL ID, name, description). It is clear on its own, but it never distinguishes itself from siblings like get_cell_type_detail or search_locations, so an agent must infer which one to pick.

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

Usage Guidelines2/5

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

There is no explicit when-to-use statement and no mention of alternatives such as search_locations (for querying by keyword) or get_location_detail (for a single SL entry). The description implies a lookup-by-cell-identifier pattern but leaves routing entirely to inference.

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