Skip to main content
Glama

Asana

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.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint as true, and destructiveHint false. Description adds default model details, optional BYO key for Anthropic with cost implications, and return structure (per-model score/confidence/signals/raw_response + combined view), fully complementing annotations.

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?

Description is front-loaded with purpose and provides essential details in a compact paragraph. Slightly long but every sentence is informative.

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 missing output schema, description fully explains return format. Covers all aspects: action, parameters, defaults, use cases, and behavioral expectations.

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%, but description adds value by explaining default model (Workers AI Llama-3.3-70b free), cost implications of _apiKey, and the role of context parameter for disambiguation, going beyond schema descriptions.

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?

Description clearly states the tool probes LLMs for knowledge about a business/brand/product/topic and scores visibility (0-100). It distinguishes from siblings focused on general research or entity comparison by emphasizing AI visibility scoring.

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?

Specifies use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and default model behavior. Could explicitly mention when not to use (e.g., for general research), but context is clear.

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

All Asana 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

B3.3/5.0
Disambiguation2/5

The tool set mixes Asana project management tools with a large number of Pipeworx data retrieval tools. Within the Pipeworx subset, tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded have overlapping purposes, and multiple prediction market tools exist (e.g., polymarket_arbitrage, polymarket_edges). This creates ambiguity and potential for misselection.

Naming Consistency2/5

Tool names lack a consistent pattern. Some use 'asana_' prefix, others use descriptive phrases (e.g., 'compare_entities', 'generate_llms_txt'), and some are single verbs (e.g., 'forget', 'remember'). Mix of different conventions leads to unpredictability.

Tool Count2/5

37 tools is high for a server named 'Asana', yet only 6 tools are Asana-specific. The majority are Pipeworx tools unrelated to Asana. This overloading makes the server feel bloated and off-purpose.

Completeness2/5

For Asana functionality, the set is incomplete: missing update/delete task, project management features, etc. The Pipeworx tools are extensive but irrelevant to the server's stated purpose, leaving the Asana workflow with notable gaps.

Resources