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Oracle Conviction

get_oracle_conviction
Read-only

Oracle conviction score for a pump.fun mint: the fused 0-100 score the Oracle engine produces by combining four intelligence pillars: pedigree (who bought it), structure (organic vs bundle, holder concentration, bubblemaps connectivity), narrative (category, meme virality, news hook), and momentum (timing, velocity). Returns the tier (prime ≥86 / strong ≥72 / lean ≥54 / watch ≥36 / avoid), the per-pillar breakdown, the natural-language reasons that drove the score, the full "who's in" trader roster with reputation labels and win-rates, and the narrative read (category, virality, tags). Call this when you need the highest-confidence trade signal; it synthesises everything get_coin_intel exposes into one actionable score. Pairs with get_coin_intel for raw signals.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
mintYesSolana mint address (base58).
networkNomainnet

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoOracle deep-link URL for this coin.
mintNo
nameNo
foundYes
symbolNo
outcomeNoLabeled outcome if available.
whos_inNoClassified trader roster — every wallet that touched this coin, annotated with their reputation.
narrativeNoNarrative classification for the coin.
convictionNoFused conviction verdict. null if the Oracle has not yet scored this coin.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds substantial behavioral context: the four intelligence pillars (pedigree, structure, narrative, momentum), the tier thresholds, and the comprehensive return payload. It does not disclose potential rate limits or caching, but goes well beyond annotation basics.

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?

The description is moderately long but every sentence adds value: purpose, components, return details, usage guidance, and sibling pairing. It front-loads the core function and follows with specifics. It could be slightly tighter, but it is well-organized and free of filler.

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

Completeness4/5

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

Given an output schema exists, the description doesn't need to enumerate return fields, but it still details the tier ranges, pillar breakdown, trader roster, and narrative read. It covers complexity well and tells the agent exactly what to expect, making it complete enough for selection and invocation.

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 50%: mint is described, network is not. The description names the mint implicitly ('for a pump.fun mint') but does not explain the network parameter or its options. Since network has an enum and default, the schema carries some meaning, but the description does not compensate for the missing parameter description.

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—'get'—and resource—'pump.fun mint'—and clearly states the tool's output: a fused 0-100 conviction score, tier, per-pillar breakdown, reasons, roster, and narrative. It distinguishes from sibling get_coin_intel by positioning itself as a synthesis of raw signals into one actionable score.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'Call this when you need the highest-confidence trade signal.' It also provides an alternative/raw-signal counterpart: 'Pairs with get_coin_intel for raw signals,' implying when-not to use it (when you just need raw data). This meets the bar for explicit context.

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

A3.5/5.0
Disambiguation4/5

Most tools have distinct purposes, but get_coin_intel and get_oracle_conviction overlap in providing intelligence signals; descriptions help differentiate but still may cause confusion.

Naming Consistency3/5

Naming patterns are mixed: some use snake_case (get_bonding_curve), others use camelCase (sns_reverseLookup), and kol_leaderboard lacks a prefix that others like pumpfun_ have.

Tool Count4/5

19 tools cover a broad range of pump.fun functionality, from token details to claims and social analysis; the count feels appropriate for the scope.

Completeness5/5

The tool set covers virtually all core aspects of pump.fun ecosystem: token info, intelligence, trading, claims, AMM quotes, metadata upload, SNS, and social sentiment, with no obvious gaps.