Skip to main content
Glama

Simap

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds cost/API key requirements for Anthropic, the default model, and return structure. Does not mention rate limits or error behavior, but annotations already cover safety.

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?

Four sentences, each serving a distinct purpose: purpose+scoring, default+api key, return format, use cases. Zero wasted words; information density is high and front-loaded.

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?

Despite no output schema, describes per-model return fields (score, confidence, signals, raw_response) and combined view. Covers all 4 parameters with examples. Adequately complete for a probe tool with moderate complexity.

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%, so baseline is 3. Description adds value by clarifying default behavior for 'models', the role of '_apiKey' (pass-through to Anthropic), and disambiguation for 'entity' and 'context' with examples.

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 uses a specific verb ('probe'), resource ('LLMs'), and outcome ('score visibility 0-100 per model'). It clearly distinguishes from siblings like 'scan_competitor_ai_presence' by emphasizing per-model scoring and default model behavior.

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

Usage Guidelines4/5

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

Explicitly lists use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the free default vs. paid Anthropic probe. Lacks exclusions or alternatives beyond the implicit sibling differentiation.

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

All Simap toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation2/5

There are several tools with overlapping query responsibilities: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-variants of the same router, and bet_research, polymarket_edges, and polymarket_arbitrage all target prediction-market opportunity discovery. An agent must read the long descriptions carefully to distinguish them, and some variants are behaviorally identical today.

Naming Consistency3/5

All names are readable snake_case, but the verb_noun convention is not consistently applied: some are clean verb phrases like validate_claim and discover_tools, while others are noun phrases like entity_profile, recent_alerts, or simap_project. The prefixed groups (polymarket_*, pipeworx_*) help, but the overall naming style is mixed.

Tool Count2/5

34 tools is above the heavy range, and the set is inflated by redundant router variants, five overlapping Polymarket tools, and loosely related utilities like generate_llms_txt and scan_dependency. The server is named Simap but only three tools relate to Swiss procurement, making the scope feel bloated and misaligned.

Completeness4/5

For an information-retrieval-style server, the core workflows are well covered: discovery, routing, grounded answers, entity resolution, profiles, comparisons, fact-checking, subscriptions, and memory. The main gaps are minor, such as updating a subscription in place or deeper native Simap-specific actions, and those can be worked around.

Resources