Skip to main content
Glama

Osf

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
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive nature. The description adds useful context: default free model, pay-for-Anthropic, return structure (per-model score/confidence/signals/raw_response + combined view). No contradictions.

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?

Three well-structured sentences: purpose and output, model details and pricing, use cases. Front-loaded with key info, no wasted words.

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 having no output schema, the description adequately describes the return structure (per-model + combined view). All parameters are explained, use cases are given, and the tool's behavior is sufficiently documented for selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the description still adds value by clarifying the entity parameter with examples, explaining the models array and _apiKey usage, and the context parameter for disambiguation. This provides a richer understanding beyond the schema.

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 clearly states the tool probes LLMs for brand visibility and scores it (0-100), distinguishing it from siblings like ask_pipeworx or deep_research which are more general Q&A or research tools.

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?

The description provides clear use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains the model selection and API key requirement, but does not explicitly mention when not to use the tool or suggest alternatives for different needs.

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

All Osf 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

A3.5/5.0
Disambiguation2/5

Several tools have near-identical or heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx and validate_claim both answer factual questions, and discover_tools and suggest_questions both surface capabilities. The dense set of Polymarket and entity-research tools further blurs boundaries despite long descriptions.

Naming Consistency3/5

Names are consistently snake_case, but they do not follow a single predictable verb_noun pattern: actionable names like ask_pipeworx, search_projects, and compare_entities mix with noun-style names like polymarket_edges, pipeworx_feedback, and recent_changes. The convention is readable but not uniform.

Tool Count2/5

34 tools exceeds the reasonable scope for a coherent server, and many are near-duplicate variants or members of sprawling tool families (ask_pipeworx variants, multiple Polymarket scanners, several meta/discovery tools). The set would be stronger with consolidation around a smaller number of distinct capabilities.

Completeness1/5

If this server is meant to provide OSF access, the surface is severely incomplete: only search_projects, search_preprints, and get_project exist, with no create/update/delete, file handling, registration, or contributor access. If it is meant to be a broader Pipeworx assistant, the OSF tools are unrelated and the domain is so scattered that coverage is incoherent.

Resources