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Enterprise Supply Graph Visualization Agent

sg_visualization

Generates global multi-tier supply-chain graphs providing full visibility into enterprise and product dependencies.

Pricing: {"unit": "credits", "per_run": 264590}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pidYesInternal company ID for the target enterprise, obtained from the search_company_candidates MCP tool (e.g. a77828f060c866441f2403384b271e63 for Tesla, Inc.).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations only include 'openWorldHint: true', which is a minimal hint. The description adds no behavioral context beyond what's obvious (generating a graph). It doesn't explain what happens if the pid is invalid, whether it's read-only, or any side effects. Since annotations are sparse, the description could add more behavioral clarity (e.g., that it's a read-only operation).

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 description is concise: one sentence of purpose plus a pricing line. It's well-structured and front-loaded with the main purpose. The pricing line is extra but could be useful for agents to consider cost.

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?

The tool has a simple input (one required parameter) and includes an example in the schema. The description plus schema provides adequate context for an agent to understand the tool's input. However, it doesn't clarify what the output graph looks like or any limitations, but since there's an output schema (not shown), the description doesn't need to explain return values.

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 coverage is 100% because the single parameter 'pid' is described in detail within the schema, including an example value. The description adds meaning by referencing 'search_company_candidates' as a source for the ID. Given only one parameter, the schema description is sufficient, and the description doesn't need to add more.

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 clearly states the tool's purpose: 'Generates global multi-tier supply-chain graphs providing full visibility into enterprise and product dependencies.' This is specific and uses a distinct verb ('Generates') and resource ('supply-chain graphs'). It distinguishes it from sibling tools like 'sg_chokepoint' and 'supply_chain_risk_prediction' by focusing on full graph generation.

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 doesn't explicitly state when to use this tool versus alternatives. However, the input schema's parameter description ('Internal company ID for the target enterprise, obtained from the search_company_candidates MCP tool') implies the prerequisite of first searching for a company ID. This provides some context but not explicit when/when-not guidance.

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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TDQS

B3.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with consistent scopes (e.g., chain_* vs park_* vs company_* vs gov_data_*). The list/num pairs are clearly differentiated. A few overlapping concepts exist (e.g., company_patent vs enterprise_change_innovation) but descriptions clarify the angle. Some typos (company_randomin_spection) don't cause ambiguity.

Naming Consistency4/5

Naming follows a mostly predictable snake_case pattern with prefixes indicating domain (chain_, park_, company_, enterprise_change_, gov_data_, poi_data_, business_surrounding_, cbd_surrounding_). Most tools use <prefix>_<entity>_<action> or <prefix>_<subject>. A few outliers (sg_chokepoint, tariff_calc, corporate_exception_report) deviate but are few and recognizable.

Tool Count1/5

With 198 tools, this is far beyond any reasonable scope for a single server. It exceeds even the 'extreme mismatch' threshold of 50+ tools. The large number makes selection and discoverability challenging, despite good internal organization.

Completeness4/5

The tool surface covers a vast range of enterprise data, regional macro stats, POI details, supply chain analysis, and tariffs. It appears to cover the primary domain comprehensively, with only minor potential gaps (e.g., no direct tool for company debt ratings or specific product catalogs, but these are addressed via enterprise_change_* and company_* tools).

Resources