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Glama

biii

by io.github.philpof102-svg

till_vet_approach

JUDGE AN INBOUND OPPORTUNITY BY ITS ASK, NOT BY HOW GOOD IT LOOKS (podcast, interview, partnership, job, AMA). Built from a lure that worked on someone who verifies counterparties professionally: a 35-question production dossier citing his real scoring model, his settlement rails, his own catchphrase, quoting his posts verbatim — and asking genuinely HARD questions, because a flatterer never includes criticism and including it is what flips an approach from marketing to journalism in the reader's head. The mechanism is EFFORT AS A TRUST SIGNAL: that much researched detail used to cost hours of human work, so nobody spent it on one target, and everyone's instinct silently priced that in. The arithmetic was right for decades and is not right now. So this deliberately does NOT score how convincing an approach is — grading convincingness would just give a forgery a good mark. It grades the two things a forger cannot hide: where a link ACTUALLY points (a brand name to the left of the registrable domain is a free label, so wechat.web09eu.com is web09eu.com), and what the sender wants you to do. Never returns "safe".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
linksNoevery URL in the message
urgencyNowas time pressure applied?
platformNothe platform they named, e.g. "WeChat", "Zoom"
asksToInstallNo
asksForKeyOrSeedNo
asksForSignatureNo
asksForUpfrontPaymentNo

Schema Changelog

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

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

No annotations provided. Description mentions 'never returns safe' but does not clarify what it does return, whether it is read-only, or any side effects. Behavioral context is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is overly verbose with a backstory and marketing language. The key functional info is buried, and sentences could be trimmed without loss.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema and 7 optional parameters with low coverage. Description does not explain what the tool returns beyond 'never returns safe', leaving a major gap for agent understanding.

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

Parameters3/5

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

Schema coverage is only 43% (descriptions for 3 of 7 parameters). Description adds context about 'where a link actually points' and 'what the sender wants' linking to boolean fields, but does not fully compensate for missing parameter descriptions.

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?

Description states it 'judges an inbound opportunity by its ask' and distinguishes from other vet tools by focusing on the approach message itself. However, the long narrative and lack of concise statement reduce clarity.

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?

Some guidance is given (does not score convincingness, grades link and ask), but no explicit when-to-use or when-not-to-use compared to sibling tools like till_trust or till_kya.

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

B3.1/5.0
Disambiguation3/5

Many tools have distinct, well-named purposes (vet_agent vs vet_merchant), but there is notable overlap between till_trust and till_vet_merchant (both provide trust verdicts), and till_launch_funder vs till_funder_history are closely related. The descriptions are detailed enough to differentiate, but an agent could still misselect between a few pairs.

Naming Consistency3/5

All tools share the till_ prefix, but the pattern is mixed: some use verb_noun (check_invoice, create_charge, watch_wallet) while others are noun phrases (key_exposure, open_approvals, rug_powers) or bare nouns (floor, trust, roll). This is readable but not predictable, so an agent cannot reliably guess a tool name from a verb.

Tool Count2/5

At 29 tools, this server exceeds the 25+ threshold that signals an overgrown toolkit. Even with a broad domain, many tools are one-off niche scanners (till_b20_authentic, till_floor, till_meter) that inflate the surface and could be consolidated or externalized.

Completeness4/5

The toolkit covers the payment lifecycle comprehensively: create charges/invoices, check payments, verify delivery, generate receipts, rolls, and accounting exports. It also spans identity, trust, security scanning, and theft tracing. Minor gaps exist (no update/cancel for charges, no token-general vetting), but the non-custodial, read-only design makes these acceptable.

Resources