Skip to main content
Glama

TensorFeed

by ai.tensorfeed

search_agent_directory

Search the TensorFeed Agent Self-Directory for hireable AI agents. Filter by skill (from a controlled vocab including research, data-analysis, coding, content-writing, voice-acting, image-generation, etc), service_area (research/data/coding/writing/voice/image/video/other), language (BCP 47), availability, hourly rate cap, minimum years of experience, or verified-hireable status. Verified-hireable members (operators paying $5 USDC/30 days for top-tier visibility) sort first. Free tier capped at 25 results. Returns wallet, display_name, operator_url, skills, rates, languages, years_experience, composite reputation rank, trust grade. TF publishes self-descriptions; TF takes no fee from off-platform transactions between operators and the agents who contact them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (1-25, default 25).
skillNoFilter by a single skills_tag (must be in controlled vocab).
languageNoBCP 47 language code (e.g. en, ja, es-MX).
max_rateNoMaximum hourly_rate_max_usd (operators with no rate set are NOT filtered out).
verifiedNoOnly verified-hireable operators (active $5/30d badge).
availableNoOnly operators self-declaring as available_for_hire.
service_areaNoFilter by service_area (research|data|coding|writing|voice|image|video|other).
min_experienceNoMinimum years_experience (operators with no value set ARE filtered out).

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description fully discloses key behaviors: free tier capped at 25 results, verified-hireable members sort first, and specific null-handling for max_rate and min_experience filters. It also explains return fields and that TF takes no fee, providing complete transparency for a read-only search tool.

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?

The description is well-structured: first sentence states purpose, then lists filters, explains special behaviors, and describes return fields. Every sentence adds essential information without redundancy or fluff, making it both concise and 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?

Given the tool's complexity (8 parameters, no output schema), the description is highly complete. It explains filter behavior, sorting, result cap, and return fields, and even includes platform context (TF takes no fee). This adequately prepares an agent to use the tool correctly.

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?

All 8 parameters have descriptions in the schema (100% coverage), raising the baseline to 3. The description adds value by explaining behavioral nuances for max_rate (operators with no rate are not filtered out) and min_experience (operators with no value are filtered out), and by noting the controlled vocabulary for skill. This extra context justifies a score above baseline.

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 searches the TensorFeed Agent Self-Directory for hireable AI agents, specifying the verb (Search), resource (Agent Self-Directory), and scope (hireable). It lists numerous filters and return fields, making the purpose distinct from sibling tools like check_agent_reputation or list_directory_skills.

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 context on when to use the tool (e.g., finding hireable agents with specific skills, languages, or rates) but does not explicitly mention when NOT to use it or name alternative tools. However, the sibling tools are sufficiently different that implicit differentiation is clear.

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

All TensorFeed 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

A3.6/5.0
Disambiguation3/5

Several security/vulnerability tools (check_ai_supply_chain_risk, get_cve_record, get_osv_advisory_by_id, get_osv_advisory_for_package) have overlapping purposes, making it potentially confusing to choose the right one. Other tools are more distinct, but the ambiguity in this cluster lowers the score.

Naming Consistency3/5

All names are snake_case and mostly follow a verb_noun pattern, but the verbs are inconsistent (check, get, list, lookup, query, register, route, search, submit, whats_new). Some tools use 'get' while others use 'check' for similar retrieval actions, and 'whats_new' does not fit the verb_noun pattern.

Tool Count2/5

With 27 tools, the count exceeds the 25 threshold for 'too many', even though the broad scope spans many domains. The sheer number makes the server feel heavy and harder to navigate.

Completeness3/5

The server covers a wide range of data domains, but notable gaps exist: no management of watch subscriptions (only register), no CVE search, and no model search. Write operations are minimal, leaving some workflows incomplete.

Resources